# On the Definition of Japanese Word

Yugo Murawaki
Graduate School of Informatics, Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan
<EMAIL_ADDRESS>
###### Abstract

The annotation guidelines for Universal Dependencies (UD) stipulate that the
basic units of dependency annotation are _syntactic words_ , but it is not
clear what are syntactic words in Japanese. Departing from the long tradition
of using phrasal units called _bunsetsu_ for dependency parsing, the current
UD Japanese treebanks adopt the Short Unit Words. However, we argue that they
are not syntactic word as specified by the annotation guidelines. Although we
find non-mainstream attempts to linguistically define Japanese words, such
definitions have never been applied to corpus annotation. We discuss the costs
and benefits of adopting the rather unfamiliar criteria.

##  1 Introduction

Japanese occupies a unique position in text processing because unlike Chinese
and Thai, it is morphologically rich and yet does not use white space to
delimit words. It is no wonder that Japanese language processing (JLP) has
developed its own peculiar task designs. To facilitate text processing in
multilingual settings (Buchholz and Marsi, 2006), however, Japanese must be
analyzed in a manner comparable to other languages.

The Universal Dependencies (UD) project (McDonald et al., 2013) provides a
rare opportunity to overhaul grammatical treatments of Japanese because
otherwise the research area is considered too mature to change. Here we focus
on the lowest layer of corpus annotation, that is, the definition of word.
Departing from the long tradition of using phrasal units called _bunsetsu_ for
dependency parsing (Kurohashi and Nagao, 1994), Universal Dependencies for
Japanese (UD Japanese) (Tanaka et al., 2016; Asahara et al., 2018) adopts
short, morpheme-like units called the Short Unit Words (SUWs) (Maekawa et al.,
2014).

Since UD considers morphology to be word-internal domains, UD Japanese clearly
violates the current UD guidelines. However, we argue that SUWs are an
understandable tentative solution. To define UD-compliant words, we need to
classify dependent (function) morphemes into (1) affixes that are part of
other words and (2) clitics that are treated as separate words (Universal
Dependencies contributors, 2019b). Although there are attempts to apply the
distinction to Japanese (Hattori, 1960; Miyaoka, 2015), they remain virtually
unknown to JLP and Japanese corpus studies.

After reviewing word-like units that have been used in JLP and and Japanese
corpus studies, we show that, as exemplified in Figure 1, if we approximate
Japanese morphemes by SUWs, it is possible to create syntactic words by
combining one or more SUWs. However, we expect the cost of newly creating
workable annotation guidelines to outweigh the benefit of enabling cross-
linguistic comparison.

Note that Pringle (2016) provides an excellent review on the definition of
word and UD Japanese. We urge readers to read it first. Here we try to
complement it but we may still miss some important information since we are
connecting segregated research fields. We hope that this article will serve as
a basis for future discussion.

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she ‘Her beauty is not lost’ SUWs: | 彼女 | の | 美し | さ | が | 失わ | れ | ない | Syn. words: | 彼女 | の | 美し さ | が | 失わ れ ない | LUWs: | 彼女 | の | 美し | さ | が | 失わ | れ | ない | _Bunsetsu_ : | 彼女 の | 美し さ が | 失わ れ ない | Figure 1: Japanese syntactic words as concatenations of SUWs (Short Unit Words). A glossed example is followed by various segmentations. The vertical bars indicate unit boundaries. We can see that syntactic words (2nd row) are different from existing units such as LUWs (Long Unit Words) (3rd row) and _bunsetsu_ (last row).

##  2 UD Annotation Guidelines on Words

UD treats words as the atoms of syntactic analysis and calls them _syntactic
words_. The guidelines on tokenization and word segmentation devote much space
to distinguishing syntactic words from orthographic and phonological words
(Universal Dependencies contributors, 2019b). However, there are no
orthographic words in Japanese and phonological words are irrelevant to JLP.

In our understanding, the key assumption behind syntactic words is that there
is a boundary between morphology and syntax. Although the guidelines do not
make it explicit, this entails the manual task of classifying dependent
morphemes into affixes and clitics. The former are part of words while the
latter are words by themselves.

UD’s decision is based on the lexicalist hypothesis that grew out of the early
stages of generative grammar (Chomsky, 1970). It is important to note that
this hypothesis does not necessarily have strong theoretical support. There
are two extreme positions: one recognizes morphology and syntax as distinct
domains (Di Sciullo and Williams, 1987) and the other denies the autonomy of
morphology and incorporates morphology into syntax (Lieber, 1992). UD appears
to lean toward the former but most theoretical analyses fall somewhere in
between. Moreover, even if the autonomy of morphology is accepted to a certain
degree, context-free grammar (CFG) trees are often employed to analyze word
formations (Kageyama, 1993).

##  3 _Bunsetsu_ in Traditional JLP

The traditional JLP pipeline is morphological analysis, _bunsetsu_ chunking,
and dependency parsing in this order. What is conventionally referred to as
morphological analysis in JLP is the joint task of segmentation, part-of-
speech (POS) tagging and lemmatization. Morphemes, often simply referred to as
“words” in the literature, are not necessarily morphemes in the sense of
linguistics. In the standard grammar-and-dictionary approach, subdividing text
into smaller productive elements improves the dictionary’s coverage while
adding longer fixed elements (e.g., proper nouns and unproductive compounds)
to the dictionary mitigates errors during analysis. Morphemes are the outcome
of engineering decisions to achieve a balance between the two. For this
reason, morphemes are not explicitly defined in, for example, the Kyoto
University Text Corpus (Kurohashi and Nagao, 1998) but the default dictionary
of the morphological analyzer JUMAN (Kurohashi et al., 1994) serves as the de
facto criteria. The resulting lack of consistency in segmentation is
criticized by corpus linguists (Maekawa et al., 2014).

_Bunsetsu_ chunking is usually performed as a preprocessing of dependency
parsing. Originating from the Japanese grammatical tradition (Hahimoto, 1933),
_bunsetsu_ is a relatively stable111 Most disagreements come from treatment of
grammaticalized elements.  phrasal unit, typically consisting of one or more
_content morphemes_ followed by zero or more _function morphemes_.222To be
precise, there are a small number of prefixes and proclitics in Japanese. In
_bunsetsu_ chunking, they are attached to content morphemes.  Note here that
there is no distinction between affixes and clitics. In example (1), both the
nominalizer suffix さ and the case enclitic が are part of the same _bunsetsu_
($||$ denotes _bunsetsu_ boundaries).

  * (1)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful

After _bunsetsu_ chunking, dependency relations are assigned to _bunsetsu_
pairs.333 The CoNLL-2009 shared task on syntactic and semantic dependencies
(Hajič et al., 2009) adopted morpheme-based dependency trees for Japanese.
These trees were constructed by automatically dissolving _bunsetsu_ chunks.

##  4 NINJAL’s Units

The SUWs, which were chosen as the atoms of syntactic relations by UD
Japanese, have come out of more than six decades of corpus studies by the
National Institute for Japanese Language and Linguistics (NINJAL). It is worth
noting that although gradually adopting NLP technologies, corpus linguistics
remains culturally very different from NLP. For example, tedious manual labor
is often preferred over less reliable but orders of magnitude faster automatic
analysis. Given the fact that parsing has become so mature that the focus of
corpus building in JLP has shifted to zero anaphora, discourse and other
higher-level structures (Hangyo et al., 2012; Kawahara et al., 2014), NINJAL
is perhaps the only institution in Japan that can reinvent the wheel.

SUWs are one of several units created by NINJAL. Other units relevant to the
present discussion are LUWs (Long Unit Words) and _bunsetsu_.444 The remaining
units are Middle Unit Words (designed for speech research) and Minimal Unit
Words (used to create SUWs). The predecessors of SUWs and LUWs were created
out of an urgent need to count word-like units in text in a consistent way.
When defining these units, NINJAL declared itself agnostic with theoretical
linguistics and maintained what it called an operationalist approach. This
resulted in book-length guldelines with literally hundreds of rules (Ogura et
al., 2011) but none of them were justified on linguistic grounds.555 We
previously worked on acquiring unknown morphemes (those missing from the
dictionary) from text (Murawaki and Kurohashi, 2008). While our system
operated in conjunction with the default dictionary of the morphological
analyzer JUMAN (Kurohashi et al., 1994), we explored the possibilities of
making it BCCWJ-compatible. However, we gave up the plan because the
guidelines were too complex for a computer to comply with.  They sometimes
lead to counter-intuitive outcomes. For example, 寿司屋 (sushi shop) is one SUW
but ラーメン  屋 (noodle shop) is two SUWs (  marks SUW boundaries in in-line
examples). What NINJAL emphasizes is that as long as we adhere to the rules,
the number of SUWs is counted consistently.

SUWs, LUWs and _bunsetsu_ have inclusion relations in this order. A _bunsetsu_
contains one or more LUWs, and an LUW in turn contains one or more SUWs. SUWs
are morpheme-like666 Although SUWs are generally shorter than or equal to what
are treated as words in traditional JLP, an SUW is created by concatenating
one or more Minimal Unit Words. Thus Minimal Unit Words are closer to
morphemes.  while LUWs are sometimes longer. The main differences between SUWs
and LUWs lie in treatment of compound nouns and functional expressions. In
example (2), 魚  フライ (fish fry) forms one LUW (  indicates LUW boundaries).

  * (2)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful $||$ を だけ フライ 魚
$||$ =ACC =only fry fish

Dependent SUWs are mostly kept apart.

  * (3)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful $||$ を だけ フライ 魚
$||$ =ACC =only fry fish $||$ が さ 美し $||$ =NOM -NLZ beautiful

  * (4)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful $||$ を だけ フライ 魚
$||$ =ACC =only fry fish $||$ が さ 美し $||$ =NOM -NLZ beautiful $||$ ない れ 失わ
$||$ -NEG.NPST -PASS lose

You can see that the elaborate system has no room for the distinction between
affixes and clitics.

NINJAL’s flagship corpus, the Balanced Corpus of Contemporary Written Japanese
(BCCWJ) (Maekawa et al., 2014), is annotated with the SUWs, LUWs and
_bunsetsu_. Other corpora from NINJAL such as a corpus of Meiji-Taisho-era
Japanese (Ogiso et al., 2017) generally follow the pattern. Note that NINJAL
itself chose _bunsetsu_ -based dependencies for annotating the BCCWJ (Asahara
and Matsumoto, 2016).

##  5 Short Unit Words in UD Japanese

The UD Japanese team decided to create Japanese resources through automatic
rule-based conversion from the BCCWJ and BCCWJ-compatible treebanks.777
Adaptation to the BCCWJ schema is to be performed first if existing treebanks
are not BCCWJ-compatible.  Thus, UD Japanese corpora have no way of providing
information not present in the original treebanks unless rules are rich enough
to inject external knowledge.

UD Japanese adopts SUWs as the units of syntactic annotation. One of the
initially planned use cases for UD Japanese was tree-based statistical machine
translation (SMT) (Hoshino et al., 2013) although it quickly got obsolete with
the rise of end-to-end neural machine translation (Sutskever et al., 2014;
Bahdanau et al., 2014), which outperforms SMT without using any explicit
syntactic information. To facilitate Japanese-to-English translation, Japanese
syntactic trees were desired to be represented such that they could be mapped
to those of the isolating language more transparently. Obviously, this is not
a linguistically-sound motivation but is understandable given that the last of
UD’s six goals is to “support well downstream language understanding tasks
(relation extraction, reading comprehension, machine translation, …)”
(Universal Dependencies contributors, 2019a).

The adoption of SUWs clearly violates the current UD guidelines that are based
on the lexicalist hypothesis (Universal Dependencies contributors, 2019b). We
could resort to a weaker version of the hypothesis that differentiates
inflectional and derivational morphology and recognizes syntactic nature of
inflection. Still, we have no choice but to treat derivational affixes as part
of words. 美し  さ (beauty) in example (1) is treated as two words, given POS
tags, ADJ and PART (particles), and connected with the dependency relation
mark (finite clause subordination). However, the suffix さ derives the noun
from the adjective and thus should be part of the single word with POS tag
NOUN.

##  6 Distinguishing Clitics from Affixes

As we have seen, JLP and Japanese corpus studies have not made a distinction
between affixes and clitics. It is because in traditional Japanese _school
grammar_ , word-like elements are first classified according to the
content/function distinction. The latter are then divided into conjugable
_jodōshi_ and non-conjugable _joshi_ (Hahimoto, 1933), which are orthogonal to
the affix/clitic distinction.888 Miyaoka (2015) attributes the absence of the
affix/clitic distinction in Japanese grammatical tradition to the peculiarity
of the Japanese writing system, in which logographic _kanji_ are used to write
content words while syllabic _hiragana_ follow _kanji_ to represent
inflectional endings and other functional elements. He suggests that the
content/function dichotomy obscures the formal distinction between affixes and
clitics.  Pringle (2016) gives a thorough review of grammatical studies of
Japanese in this regard.

However, this does not mean that the distinction has never been applied to
Japanese. In fact, the structuralist linguist Shiro Hattori presented a formal
classification in which the primary division is between free forms (_jiyū-
keishiki_) and bound forms (_fuzoku-keishiki_) (Hattori, 1960). Minimal free
forms, or words, are divided into independent words (_jiritsu-go_) and bound
words (_fuzoku-go_). Bound words and bound forms correspond to clitics and
affixes, respectively, in our terminology. He then presented three principles
for identifying bound words (clitics), and as by-products, bound forms
(affixes). Unfortunately, while he demonstrated the general applicability of
the principles using examples not only from Japanese but from English,
Russian, Turkish and other languages, his discussion on Japanese was so
limited that it can hardly serve as a starting point of corpus annotation.

Recently, Osahito Miyaoka, a field linguist working on the polysynthetic
language Central Alaskan Yup’ik, published a monograph on the definition of
word, with a strong structuralist flavor (Miyaoka, 2015). Unlike Hattori
(1960), he presented his own classification that covered major affixes and
clitics (Table 2 of Miyaoka (2015)) and discussed borderline cases in detail.
Although dependency grammar is clearly out of the scope of his analysis, we
think that Miyaoka (2015) can be a feasible basis for corpus annotation.

##  7 Feasibility Assessment

In this section, we examine the possibility of adopting Miyaoka’s
classification for UD Japanese. This basically implies additional rule writing
because the BCCWJ and BCCWJ-compatible treebanks are too big to discard.
Needless to say, a substantial amount of manual work is needed to extend his
classification to missing elements.

Our preliminary examination suggests that the affix/clitic distinction can be
made at the level of lexical items. For example, the negation marker ない is a
suffix when following a verb and an enclitic when negating an adjective.

  * (5)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful $||$ を だけ フライ 魚
$||$ =ACC =only fry fish $||$ が さ 美し $||$ =NOM -NLZ beautiful $||$ ない れ 失わ
$||$ -NEG.NPST -PASS lose $||$ ない 書か $||$ -NEG.NPST write

  * (6)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful $||$ を だけ フライ 魚
$||$ =ACC =only fry fish $||$ が さ 美し $||$ =NOM -NLZ beautiful $||$ ない れ 失わ
$||$ -NEG.NPST -PASS lose $||$ ない 書か $||$ -NEG.NPST write $||$ ない 寒く $||$
=NEG.NPST cold.ADV

There are one syntactic word in example (5) and two words in example (6).
However, these two can easily be distinguished because they are given
different POS tags.

One known exception is らしい. Depending on the context, it can be a derivational
suffix (-ly) or an enclitic (seem to be).

  * (7)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful $||$ を だけ フライ 魚
$||$ =ACC =only fry fish $||$ が さ 美し $||$ =NOM -NLZ beautiful $||$ ない れ 失わ
$||$ -NEG.NPST -PASS lose $||$ ない 書か $||$ -NEG.NPST write $||$ ない 寒く $||$
=NEG.NPST cold.ADV $||$ 人 $||$ らしい 男 $||$ とても $||$ person -ADJLZ.NPST man very

  * (8)

ない れ 失わ が さ 美し の 彼女 -NEG.NPST -PASS lose =NOM -NLZ beautiful =GEN she $||$ ない
れ 失わ $||$ が さ 美し $||$ -NEG.NPST -PASS lose =NOM -NLZ beautiful $||$ を だけ フライ 魚
$||$ =ACC =only fry fish $||$ が さ 美し $||$ =NOM -NLZ beautiful $||$ ない れ 失わ
$||$ -NEG.NPST -PASS lose $||$ ない 書か $||$ -NEG.NPST write $||$ ない 寒く $||$
=NEG.NPST cold.ADV $||$ 人 $||$ らしい 男 $||$ とても $||$ person -ADJLZ.NPST man very
$||$ らしい 男 $||$ どうやら $||$ は 犯人 $||$ =seem.NPST man apparently =TOP culprit

However, no disambiguation is made in the BCCWJ because it follows nominals in
both cases. We have no choice but to disambiguate every token in text.

From a processing point of view, our proposal entails a novel task in JLP:
segmentation into syntactic words. Given the existing ecosystem, the
straightforward way to do this is the pipeline of (1) segmentation into SUWs
and (2) chunking of SUWs into syntactic words. However, the very motivation
behind UD is to build a single system that works for any language, and the
community would not favor such a language-specific solution. The direct
identification of syntactic words is another option, but we anticipate a loss
in performance.999 JLP experiences a _great reset_ with the introduction of
UD. Given that high-coverage dictionaries have traditionally served as
powerful resources to achieve over 99% F-measure in segmentation (Kudo et al.,
2004; Maekawa et al., 2014), drastically lower performance of dictionary-less
universal systems (Straka and Straková, 2017) is remarkable although the
scores are not directly comparable. Dictionaries remain key ingredients in
JLP. Tolmachev et al. (2019) managed to create a dictionary-less (non-UD)
morphological analyzer that performed comparably with a dictionary-based one.
However, they distilled dictionary knowledge through a huge raw corpus
automatically analyzed by the dictionary-based anazyler.

The elimination of affixes from syntactic analysis should be compensated in
some way or other. Some of their properties can be reflected by modifying the
POS tags, but the rest are to be described as (linguistic) features. This is
not an easy task not only because UD Japanese currently does not use
linguistic features at all but also because they are new to the Japanese
dependency parsing community as a whole. Thanks to a high degree of form-
meaning transparency of the agglutinative language, it makes direct use of
lexical items as machine learning features (Uchimoto et al., 1999) and does
not bother to map them to linguistic features.

##  8 Discussion and Conclusions

As described in Section 5, UD Japanese resources are created through automatic
conversion from existing treebanks. This is partly because the team considers
that UD guidelines are not yet stabilized but subject to future changes that
may be responses to new, typologically unfamiliar language or may simply come
from improved awareness of typological studies (Croft et al., 2017), where
inherent difficulties in cross-linguistic comparison are widely recognized
(Haspelmath, 2010). While automatic conversion allows us to rapidly adapt to
changes in the guidelines, all necessary information must be present in the
original treebanks.

In order to comply with the current UD guidelines on syntactic words, we have
to add another layer of annotation to existing treebanks because they lack the
distinction between affixes and clitics. The anticipated non-negligible costs
lead us to question whether the manual work pays.

One area that is likely to benefit is cross-linguistic comparison (UD’s goal
2). Suppose that we apply cross-lingual projection of parsing models (Cohen et
al., 2011) and find that a certain pair of languages are (dis)similar.
Naturally, we want to ensure that it reflects structural properties of
languages, not arbitrary design decisions on corpus annotation. However, the
goal can be achieved only when the same level of care is given to other
languages. Besides, the distinction between affixes and clitics itself is
known to be fragile. In fact, after a detailed examination, Haspelmath (2011,
2015) concluded that there were no good criteria for defining syntactic words
as a cross-linguistically valid concept.

Another area to consider is downstream tasks (UD’s goal 6). Unless text
generation is involved, it hardly matters whether a certain function is
realized by morphological or syntactic devises. Given the semantic
transparency in the agglutinative language, unified treatment of affixes and
clitics appears to have an advantage in formal semantics (Bekki, 2010), for
example.

Rather, applications cast doubt on UD’s current framework. Although the
affix/clitic distinction is not clear-cut, subtle variations in design result
in very different representations. This also reminds us of Croft et al.
(2017)’s proposal for construction-based annotation. For example, English uses
a copula for _predicate nominal constructions_ while Russian does not, and by
directly linking content words, UD provides comparable representations. Why
not applying the same logic to the morphology-syntax division?

To conclude, we found that with manual work, UD Japanese would likely be able
to comply with the current UD guidelines on words, but we are unsure if it is
the right course of action to take.

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missing

# Time Series Prediction about Air Quality using LSTM-Based Models: A
Systematic Mapping

Lucas L. S. Sachetti1, Vinicius F. S. Mota1

###### Abstract

This systematic mapping study investigates the use of Long short-term memory
networks to predict time series data about air quality, trying to understand
the reasons, characteristics and methods available in the scientific
literature, identify gaps in the researched area and potential approaches that
can be exploited on later studies.

  * Keywords:

Long Short-term Memory (LSTM), Time Series, Air Quality, PM2.5 prediction,
Systematic Mapping

##  1 Introduction

Air pollution has become increasingly worrying for the population in recent
times. As a result, data on air quality has been gradually increasing and the
science underlying health-related impacts is also evolving rapidly [WHO 2016].
There are several air pollutants, the most common of which are Carbon Monoxide
(CO), Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2) and Particulate
Material (PM10 and PM2.5), which are pollutants monitored to make the so-
called Air Quality Index (AQI) which qualitatively says the state of the
environment. The monitoring of these pollutants is usually done on a macro-
scale by meteorological stations scattered at strategic points in cities.

These pollutant data are said to be a time series, which is a set of
observations ordered in time. This makes it possible to use analytical methods
to tell whether the air quality is good for the population or not. Among these
methods there is descriptive analysis, which only describes current and past
events. Predictive analysis, which uses past data to try to predict future
actions. There is also prescriptive analysis, which uses predictive and/or
descriptive analysis to make or recommend decisions based on the results
obtained by the analyzes.

There are some ways to make data prediction, including the use of artificial
neural networks (ANN) that are commonly used to recognize patterns [Crone and
Kourentzes 2010]. As the data have a strong relationship with time, one of the
most recommended networks is a recurrent neural network (RNN) [Boné et al.
2003] which uses previous information to serve as an understanding of the
current data in the neural network. With the wide application of time series
models based on the RNN, greater precision of prediction and the long short-
term memory recurrent network (LSTM-RNN) [Azzouni and Pujolle 2017] has been
proposed. LSTM-RNN not only retains the timing characteristics of the RNN
structure, but also has the memory function for time series.

The systematic mapping presented in this paper investigates the following
questions: (i) the vehicle and dates of publication of the studies; (ii) the
contexts of problems that have been addressed in LSTM networks related to Time
Series; (iii) the LSTM models that are being used to treat problems in Time
Series; (iv) effectiveness of LSTM models in predictions; (v) which are the
hyperparameter configurations of the networks; (vi) which air quality
characteristics are being used the most; (vii) what other neural network
methods are being used in conjunction with the LSTM. This mapping was
structured in 7 research questions, where 155 studies were selected and
analyzed, 57 of which were approved by reading the abstract and keywords, and
from these, 12 had the data extracted, according to the systematic mapping
method used.

This article is organized as follows. Section 2 presents a theoretical
foundation about the research. Section 3 the research protocol used. Section 4
the main results. Section 5 a discussion of the results. Section 6 concludes
the work.

##  2 Background

In this section, to aid in the decision-making of research questions, which is
the object of systematic mapping, and define the scope of our investigation,
we discuss some of the key concepts in the research areas studied (LSTM).

###  2.1 LSTM

The original LSTM idea was initially proposed by [Hochreiter and Schmidhuber
1997] and since then different models have been proposed to improve the
performance of LSTM [Cho et al. 2014], [Li et al. 2019], [Karevan and Suykens
2020], [Hu et al. 2020]. The LSTM uses a locking mechanism to control the
information that must be maintained over time, the duration that must be
maintained and the time that can be read through the memory cell.

The LSTM recurrent neural network consists of a module with memory cells that
can learn data characteristics in the time domain. It has been widely used in
many fields due to its improved processing performance for time series data.
The memory module in the LSTM recurrent neural network contains three
multiplication units: an input gate, a forgetting gate and an output gate.
These gates control the input, update and output of information, respectively,
so that the network has a certain memory function. On the other hand, the
network has more learning parameters due to these gates.

##  3 Research Protocol

This systematic mapping study was performed following the guidelines proposed
by the systematic literature reviews that involve three main phases [Keele et
al. 2007]. The Planning phase refers to the preview activities and aims at
establishing a review protocol defining the research questions, inclusion and
exclusion criteria, study sources, search string, mapping procedures and the
control papers. These control papers are works that closely match with the
objectives of the study and the inclusion and exclusion criteria used to
define the selected papers. Thus, the control papers must be part of the
selected works.

The Conducting phase consists of searching, selecting and analyzing the
studies, in order to extract and synthesize their data. This phase executes
the plan defined in the Planning phase. The last is the Report phase, which is
characterized by the writing of the results to disseminate them. The main
results from the first phase are presented below.

###  3.1 Research Questions

This mapping aims at answering the following research questions:

  * RQ1.

When and where have the studies been published?

  * RQ2.

Which contexts have been most approached in LSTM networks related to Time
Series?

  * RQ3.

Which LSTM models are being used to treat time series problems?

  * RQ4.

What hyperparameters have been used in LSTM networks?

  * RQ5.

What air quality characteristics have been most approached?

  * RQ6.

What other neural network methods are being used in conjunction with LSTM?

  * RQ7.

Are LSTM models effective in predicting Time Series?

###  3.2 Inclusion and Exclusion Criteria

Only 1 inclusion criteria (IC) and 8 exclusion criteria (EC) were created. The
inclusion criterion is: (IC1) The study uses LSTM in data time series on air
quality. The exclusion criteria are: (EC1) The study does not have an
abstract; (EC2) The study is just published as an abstract; (EC3) The study is
not written in English; (EC4) The study is an older version of another study
already considered; (EC5) The publication is a tutorial, events annals,
lecture log or secondary study; (EC6) It was not possible to access the study;
(EC7) The study does not satisfy the inclusion criteria; (EC8) Does not use
data about PM2.5 or PM10.

###  3.3 Sources

The search was applied to 7 electronic databases. However, it was not possible
to export data from some databases used, so the results of the research of
only two of them were used, which are:

  * •

Scopus (http://www.scopus.com)

  * •

IEEE Xplore (https://ieeexplore.ieee.org)

###  3.4 Keywords and Search String

The search string used in this study considered three areas: the LSTM neural
network, Time Series and Air Pollution (see Table 1), and was applied to three
metadata fields (title, abstract and keywords). The string has been refined
about 7 times to fit the proposed study topic.

Table 1:  Keywords and Search String Area | Keywords
---|---
LSTM | ”LSTM”, ”long short-term memory”, ”prediction”, ”prescription”
Time Series | ”Time Series”, ”forecasting”, ”forecast”
Air Pollution | ”air”, ”pollution”
Search String: ((”LSTM” OR ”long short-term memory”) AND ”Time Series” AND
(”forecast*” OR ”prediction” OR ”prescription”) AND (”air” OR ”pollution”))

###  3.5 Data Storage

The studies returned in the searching phase were cataloged appropriately. This
helped to classify and analyze the studies for the next phase more clearly and
intelligently.

###  3.6 Assessment

The mapping protocol was tested to verify its feasibility and adequacy, based
on a pre-selected set of studies considered relevant for the investigation.
The review process was conducted by the first author from this paper, who
carried out its validation. About 37% of the studies were analyzed.

##  4 Conduction the Mapping

In this section, we discuss the main steps of this research, namely: search
and selection, and synthesis and data analysis.

###  4.1 Search and Selection

Only articles from 2013 onwards were considered because as the theme is
recent, no publications were found before this date. As a result, 155
publications were returned, of which 109 from Scopus, and 46 from IEEE Xplore.

A selection process for the returned publications was applied, which was
divided into 3 stages. In the first, duplicates are eliminated based on
examining the title and abstract, and the number of publications has been
reduced to 123 (20.64% reduction). In the second step, the inclusion and
exclusion criteria were imposed considering the title and abstract. 65
publications (52.84%) were eliminated. Some papers had a slightly confused
title and abstract and made it difficult to apply the inclusion and exclusion
criteria. This made it necessary to read the introduction of these studies to
apply the criteria.

Finally, in the third step, the inclusion and exclusion criteria were applied
considering the entire text, reducing the number of publications from 58 to 12
(79.31 %). Unfortunately, many publications with excellent abstracts were
eliminated because it is not possible to have access to the entire text.
Attempts have been made to contact some of the authors of these excluded
papers, but there was still no feedback.

From these steps, we selected 12 studies that were considered relevant, 9 + 3
control papers, for the next phase: Data Analysis. Table 2 shows the steps and
results of the selection process. The selection process resulted in a
reduction of about 92%, 12 out of 155. Table 3 lists these 12 studies that
were considered relevant.

Table 2:  Selection Process Steps Results Step |  Criteria |  Analyzed Content |  Initial N. of Studies |  Final N. of Studies |  Reduction (%)
---|---|---|---|---|---
1ª |  Eliminating Duplication |  Title and Abstract |  155 |  123 |  20,64%
2ª |  IC1, EC3, EC5, EC6, EC7 |  Title and Abstract |  123 |  58 |  52,84%
3ª |  IC1, EC8 |  Entire Text |  58 |  12 |  79,31%
Table 3:  Selected Papers ID |  Reference |  ID |  Reference
---|---|---|---
[1] |  [Singh et al. 2019] |  [2] |  [Zhao et al. 2019]
[3] |  [Riekstin et al. 2018] |  [4] |  [Li et al. 2020b]
[5] |  [Lu et al. 2020] |  [6] |  [Qin et al. 2019]
[7] |  [Wu and Lin 2019] |  [8] |  [Liu et al. 2019]
[9] |  [Liu et al. 2020] |  [10] |  [Thaweephol and Wiwatwattana 2019]
[11] |  [Park et al. 2017] |  [12] |  [Li et al. 2020a]

###  4.2 Synthesis and Data Analysis

In this section, answers to research questions are presented.

RQ1. When and where have the studies been published? – The selected
publications were published between 2017 and 2020, being that in 2019 there is
a higher occupancy rate (50% of publications). In relation to type of
publication vehicle, 75% were published in Journals and 25% in Conferences,
highlighting the IEEE Access with 25% of publications.

RQ2. Which contexts have been most approached in LSTM networks related to Time
Series? – The main prediction problems were in relation to PM2.5, Figure 1
shows the relationships of the predictions made in the studies. Standing out
[Lu et al. 2020] for making predictions of the lag effect of pollutants on
respiratory diseases, and [Liu et al. 2019] for using predictions to treat
haze-fog problems.

Figure 1: Predictions made by the studies

RQ3. Which LSTM models are being used to treat time series problems? – Among
the models used in the publications, 67% were the standard LSTM and 33% were a
new version of the LSTM. In this one we highlight one of the control papers,
[Zhao et al. 2019] by LSTM-FC, and also [Li et al. 2020b] by CNN-LSTM and [Li
et al. 2020a] by AC-LSTM which is a variant of CNN-LSTM with a layer of
attention mechanisms (attention-based layer).

RQ4. What hyperparameters have been used in LSTM networks? – The
hyperparameters used vary widely in each publication, among them the number of
LSTM layers varied from 1 to 64, the nodes varied from 16 to 800 for each
layer. Batch size was 2 to 108, epochs from 20 to 639. About the activation
function, 42 % opted for sigmoid, 25% opted for ReLu and 33% did not specify.
16% used optimizer Adam, and 16% also used RMSProp, highlighting [Park et al.
2017] who used Adagrad in addition to RMSProp and compared the performance
between the two, in which it can be seen that RMSProp performed better.

RQ5. What air quality characteristics have been most approached? – Among the
pollutants approached, PM 2.5 had a greater focus with a presence in 83% of
the articles, followed by PM10 with 58%. In addition, 50% of the publications
used other pollutants related to the Air Quality Index (AQI), such as
[Thaweephol and Wiwatwattana 2019] which is able to correlate the growth of
PM2.5 with the other pollutants.

RQ6. What other neural network methods are being used in conjunction with
LSTM? – CNN, RNN and SVR (support vector regression) are the most outstanding
in relation to the networks that are being used together or for comparisons.
Figure 2 shows all the models used. The paper [Li et al. 2020a] stands out for
making comparisons with SVR, RFR (random forest regression), MLP (multilayer
perceptron), RNN, LSTM, CNN-LSTM and presenting superior results to all of
them.

Figure 2: Models used with LSTM. (GRU - Gated recurrent unit, and ELM -
Extreme learning machine)

RQ7. Are LSTM models effective in predicting Time Series? – All publications
used metrics of moving averages of the error comparing the proposed model with
other models. The metrics used were Mean Absolute Error (MAE), Mean Absolute
Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Pearson
Correlation. The variations in error reduction goes from 7.45% to 500% as in
the study [Park et al. 2017]. Another notable is [Qin et al. 2019] where
Pearson correlation reaches 0.97, which is very close to the perfect one where
1 would be the perfect correlation between the real and the predicted value.

##  5 Final Considerations

This paper presented a systematic mapping that investigates the use of LSTM
neural networks in time series data on air quality. 155 studies were
investigated and 12 were selected for further analysis. Despite the large
number of publications returned from the search engines, it is possible that
there are still publications that were not identified. A future work here is
to enlarge the number of studies to get more accurate data or merge the data
obtained by this research with others studies.

The contributions of this work are on showing and comparing the LSTM models
used on time series prediction. In this context, we highlight the following
conclusions: (i) A lot of studies in this area started to emerge after 2017,
this shows a growing concern about air quality; (ii) Some studies already show
that it is possible to create LSTM networks with unique characteristics for
specific themes, thus improving their performance when compared to the
standard model, as well as the creation of hybrid models using other neural
networks together from LSTM as seen in [Zhao et al. 2019], [Li et al. 2020b] e
[Li et al. 2020a]; (iii) Unfortunately, not all studies specified their
network hyperparameters, but it is still possible to notice a preference in
the activation function, sigmoid and ReLu, and in the optimizer, Adam and
RMSProp; (iv) The effectiveness of LSTM models for predicting time series is
remarkable in comparison with the statistical models in the literature and
some other models of neural networks. Most of the studies obtained gains above
50% in the reduction of errors of the metrics MAE, MAPE and RMSE.

It is undeniable that concern with air quality is one of the areas that has
grown the most in this area in recent years. And, as we can see, the use of
LSTM models is, in fact, positively influencing the prediction of the main air
pollutants. With the growth of IoT technologies and the advancement of
artificial intelligence, it is promising to say that these improvements tend
to become more and more optimized and with more accurate tools it is possible
to improve the quality of life in relation to air quality.

## References

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11institutetext: M.B. Dermatology clinic, Bizerte, Tunisia 22institutetext:
Department of Dermatology, Razi Hospital, Manouba, Tunisia 33institutetext:
AIPLabs / AIPDerm, Budapest, Hungary

# A 35-Year Longitudinal Analysis of Dermatology Patient Behavior across
Economic & Cultural Manifestations in Tunisia, and the Impact of Digital Tools

Mohamed Akrout corresponding author<EMAIL_ADDRESS>Hayet Amdouni 11 Amal
Feriani 11 Monia Kourda 22 Latif Abid 33

###### Abstract

The evolution of behavior of dermatology patients has seen significantly
accelerated change over the past decade, driven by surging availability and
adoption of digital tools and platforms. Through our longitudinal analysis of
this behavior within Tunisia over a 35-year time frame, we identify behavioral
patterns across economic and cultural dimensions and how digital tools have
impacted those patterns in preceding years. Throughout this work, we highlight
the witnessed effects of available digital tools as experienced by patients,
and conclude by presenting a vision for how future tools can help address the
issues identified across economic and cultural manifestations. Our analysis is
further framed around three types of digital tools: “Dr. Google”, social
media, and artificial intelligence (AI) tools, and across three stages of
clinical care: pre-visit, in-visit, and post-visit.

###### Keywords:

Dermatology, e-Health technology, reverse image search, social media groups,
AI tools.

##  1 Introduction

In this accelerating and expanding digital age, information technologies and
image processing techniques have thrown us into an inevitable “iconosphere”
whose psychological, epistemological, and cultural impacts are undeniable
[13]. Amongst the various industries where the digital age has transformed the
respective processes, products, and services, healthcare has significantly
lagged all other industries despite it having the greatest potential, both in
terms of the ability to fundamentally improve the delivery of care as well as
the overall impact of that to civilization. Despite the connectivity potential
and advanced digitization in this new age, patients are still at the mercy of
inaccessibility to accurate clinical care in a timely or convenient manner.
The potential transformative impact of this digital age is especially
exciting, yet thus far disappointing, in lower socio-economic regions of the
world, where economic and cultural barriers uniquely inhibit patients’ access
to the requisite care.

A patient’s healthcare “journey” has been significantly impacted by software
technology [7]. Patient “empowerment” has been augmented due to the easy
capture of skin lesions, taken by mobile phones or digital cameras from a
distance, and the proliferation of those “macroscopic” images in search
engines and social media. This empowerment by technological advances is
disseminated and encouraged by the media, which is driving patients to be more
demanding in their education and in the vocalization of their care with their
physicians.

With healthcare challenges facing the world significantly worsened by COVID,
there is an increasing imperative to better analyze and understand how digital
tools can transform healthcare across various economic and culturally distinct
regions. To effectively design and deploy such tools, it is important that we
analyze historical patient behavior across the three clinical stages (pre-,
in-, and post-visit) over an extended time frame, and in unique cultural and
economic landscapes. This is especially the case in underdeveloped countries
such as Tunisia, where healthcare services have more primitive infrastructure
and resources. In these regions, digital adoption further lags more developed
regions but there is greater potential to improve the current standard of care
and address unique economic and cultural barriers [16, 4].

By studying patient behavior over the last 35 years through the consistent
lens of two leading dermatologists in Tunisia, we identified that the first 25
of the 35 years mainly experienced slow and minimal change. However, the last
10 years witnessed a significant change due to the introduction of expansive
digital tools such as social media, Google search, reverse image searches, and
AI tools. Over that time frame, we also witnessed an economic shift in Tunisia
after the revolution in 2010-2011, and a significant deterioration in the
average household income and level of access and quality of both public and
private healthcare.

This paper makes the following contributions:

  * •

We describe the change in patients’ behavior over 35 years in Tunisia,
segmented into two periods of 1987–2012 and 2012-2022, and we pattern this
behavior across coinciding economic and cultural environments.

  * •

We discuss and highlight the witnessed effects of digital tools on patient
behavior and incorporate that impact across the three stages of clinical care
(pre-, in-, and post-visit).

  * •

We present a case for how we envision digital tools, such as “AIPDerm”, can
positively address Tunisia’s, and similar regions’, economic and cultural
issues, as well as significantly evolve the current standard of how digital
tools impact the patient’s behavior across the three clinical stages.

##  2 35 years of dermatology patient behavior across all three clinic stages

We analyze patient behavior in a single region of Tunisia across 35 years by
examining the direct experiences of two dermatologists who continuously
practiced in the region over that period. We segment the observed patient
behaviors and perceptions into the three clinical stages: pre-, in-, and post-
visit. Within each segment, we analyze the shift of those behaviors and
perceptions over the 35-year period, and more specifically, the more notable
period of 2012-2022.

###  2.1 Pre-visit Perception

Preceding the knowledge-at-your-fingertips era of search engines, it was
significantly difficult to access any breadth or depth of medical information,
constraining patients to almost exclusively trust the diagnosis of the
dermatologist. Following the launch and evolution of search engines, patients
were more quickly catalyzed at the slightest illness manifestation or concern,
with patients now empowered to match their symptoms and be pre-emptively
guided on the possible diagnoses, the urgency of the pathology, and the
therapeutic possibilities. This empowerment of manually navigating search
engines for textual matching of symptoms and conditions has more recently
evolved into taking photos of their skin lesions and using reverse image
search functionality to find cases with similar visual representations and the
corresponding possible skin diagnoses.

Around the 2010-2011 period, Facebook gained significant scale and reach in
Tunisia, with this adoption having been validated extensively in the press and
public domain given the pivotal role it played in the Tunisian and other “Arab
Spring” uprisings [3]. On the dermatology side, Tunisian citizens are
constantly engaged on Facebook groups to exchange opinions at different
engagement levels about $i)$ existing medical prescriptions from prior medical
visits, $ii)$ personal judgment about the personality, professionalism,
availability, and expertise of the respective dermatologist, and $iii)$
personal experiences with specific treatments for a given pathology (e.g.,
acne).

While these discussions can represent useful natural language processing
datasets for future AI tools and guide the design of clinical trial
recruitment, prevention campaigns, and chronic disease management [5], the
quality of shared medical information on social media suffers from the lack of
reliability and confidentiality [10]. As the working class’s household income
and purchasing power have been falling since the 2010-2011 revolution, many
patients rely on their discussions on social media to self-educate and self-
medicate, exposing patients to various risks such as mistreatment and
triggering rashes or drug toxicity. This is in contrast to the pre-digital
stronghold era (maturity of search engines and scale-up of social platforms),
which had materially lower risk exposure of erroneous and dangerous self-
education or self-medication, and where dermatologists were granted more
exclusive trust in medical determinations. In order to mitigate some of these
risks and limitations with basic self-education, Tunisian website alternatives
(e.g., med.tn, tobba.tn) offer a dedicated medical forum where subscribed
doctors can answer medical questions posted by patients. However, doctors are
asked to not provide a specific diagnosis, with their answers instead of
revolving around reporting the severity of the patient’s case, thus providing
a more triaging type of guidance.

Certain skin conditions can be embarrassing or culturally sensitive, such as
sexually transmitted diseases (e.g., venereal vegetations, genital herpes,
genital candidiasis), which have been, and continue to be, very taboo
culturally in Tunisian society. In these cases, most Tunisian patients have
notably preferred sending a photo of their lesion accompanied by a textual
description of symptoms. This optionality is something that has been enabled
by the availability of mobile devices and platforms, and whose absence
rendered a significant proportion of the population at the mercy of rigid
cultural norms, often resulting in undiagnosed or untreated conditions.

###  2.2 In-visit Perception

Across the clinical time frame of 35 years of experience, we had extensive
exposure to both public hospitals and private clinics in Tunisia. We
characterize the profile and mindset of dermatology patients during medical
visits and classify them into the following (with potential overlapping)
categories.

Patients with higher education background This category includes patients with
higher-education and scientific backgrounds (e.g., engineers, physicians,
architects) or with a background in studying advanced literature (e.g.,
lawyers, journalists, judges, administrative executives). They expect the
dermatologist to direct them towards the right etiological and therapeutic
approach after they self-administered and pre-established a survey about their
symptoms and possible diagnosis from Google searches and other digital tools.
They also keep track of high-resolution images and videos of their skin
lesions (e.g., angioedema and urticaria) which simplify and accelerate the
diagnostic process of the dermatologist. This category of patients perceives
the role of dermatologists as the only single and final source of providing
trustworthy diagnoses and treatments. These patient profiles also tend not to
over-argue or question the dermatologist’s determination of appropriate next
steps and treatments (e.g., blood test before drug prescription).

Patients with limited income This category is the one that significantly
utilizes social media and Google searches, with those tools most impacting
their patient journey through the three clinical stages. These patients try to
actively avoid paying medical consultation fees and can excessively opt for
self-medication until a friend or pharmacist urges them to consult a
dermatologist (e.g., in front of a mole growing in size out of concern of a
melanoma diagnosis) or until their condition worsens. For most witnessed
patients in this category, dermatologists in Tunisia are seen as the last
resort after exhausting all self-medication methods. Over the past decade and
with the worsening economic landscape since the 2010-2011 revolution, there
has been an increase in patients that fit into this category and who actively
seek to avoid the medical fees of multiple visits. We have noticed that they
tend to consult a dermatologist for aggregated medical concerns within a
single medical visit, which implies that they are allowing for a build-up of
illness and manifestations over an extended period of time before acquiescing
to paying for fees associated with a single visit. They view this as allowing
them to obtain a “multiple treatment package” without the intention of
treating each of the skin diseases properly and within the appropriate time
frame. However, this practice is not prohibited by Tunisian law and tends to
drive awkwardness during dermatologist interactions.

Patients who already “know” the diagnosis While it is true that a diagnosis
can sometimes seem obvious (e.g., vitiligo, common wart, acne, or burn),
patients can often have overly inflated confidence in some of these cases and
unnecessarily argue with dermatologists on their medical decisions. The
patient consults to ensure possible therapeutic arrangements, their
effectiveness, and their aesthetic aspects. Patients often perceive the role
of dermatologists as one that is oriented toward frequent follow-ups and very
personalized medical advice, without which the patient is not satisfied. This
is because the patient can unintentionally not consider the dermatologist’s
actual core diagnosis as part of the overall medical visit. The role of the
dermatologist is to reassure the patient and track them regularly according to
the severity of the disease, its location, its extent, and its treatment
punctuality. Given that patients can be overconfident about their diagnosis,
they tend to ask questions to better understand any discrepancy between the
dermatologist’s opinion and what they have read on social media or “Dr.
Google”. Questions such as “we know the diagnosis, so why are you asking for a
blood test?” or “I saw similar photos to my case on Facebook; why are you
complicating my case?” are not uncommon.

Patients with skin conditions in intimate body locations Patients in Tunisia
having genital skin lesions often refuse to be physically examined in person.
They usually come with multiple photos taken before their visit and expect the
dermatologist to give a prescription purely based on analyzing the pre-
captured photos, as they believe that “they have nothing else”, and that it
must be sufficient for the medical examination. This request goes against the
medical code of ethics but is common behavior in Tunisia given the cultural
backdrop and sensitivity in the region around intimate body locations and
taboo topics such as sexual activity and disease contraction. These
embarrassing situations can leave doctors with a dilemma, and often find
themselves engaged in a time-consuming cultural discussion in order to
convince the patient into accepting the appropriate medical examination. For
example, in the case of Behcet’s disease, when a patient photographs their
genital ulcers and asks the dermatologist for the therapeutic solution without
being examined, the dermatologist must explain to the patient that it is
imperative to inspect for medical ulcers, pseudo-folliculitis, and even carry
out a neurological examination. Predictably, based on the cultural and sexual
sensitivities on this front, our discussions with both male and female
dermatologists in Tunisia confirm that this scenario is especially frequent
when the patient and dermatologist are of opposite genders. In general, we
observe that patients in Tunisia actively seek to validate their skin
conditions in intimate locations by sending their photos via messaging apps
before being obliged to visit the dermatologist in-person. It is worth noting
that dermatologists may find themselves in various delicate situations that
could give rise to legal interventions, such as the case of a molluscum of the
perianal region of a child suggesting sexual abuse. In such medico-legal
situations, taking photos becomes imperative and mandatory, but can also be
problematic in front of some parents who opposite it in order to avoid
cultural shame and stigmas.

Uncooperative patients Patients with lower intellectual levels, educational
backgrounds, or with limited job and career prospects, are often seen spending
time on Facebook groups and other social media platforms looking for
depigmenting miracle creams for their melasma, and doing so without the more
basic concern of worrying about having a sunscreen protecting against
ultraviolet type A (UVA), ultraviolet type B (UVB) and blue light. Some of
these patients are often seen consulting dermatologists to “achieve” their
cosmetic dreams that they conjured in their imaginations through sporadic
reading of various online sources, and thus have established unreasonably high
expectations on what can be achieved and over short periods of treatment [15].
In such cases, many patients are quick to claim that the treatment must not be
effective. It has therefore proven prudent for dermatologists in Tunisia to
take regular photos showing the temporal evolution of the disease, and present
those depictions as clear visually convincing evidence whenever the patient
questions the improvement of their skin condition or the inefficacy of the
prescribed treatment. Fig. 1 depicts the case of a patient who visited his
hairdresser to shave his beard. The hairdresser suggested to the patient to
stop the nevus’s volume from increasing by surrounding it with a line of a
fishing rod. Fig. 1(a) shows the photo of the patient during his first medical
visit where the nevus is surrounded by a line of a fishing rod. The nevus
shown in Fig. 1(b) illustrates its evolution after two months of treatment.
The patient claimed that the state of his nevus did not improve, yet it was
sufficient for the dermatologist to show him his photo in Fig. 1(a) from his
first visit to convince him of the improvement.

(a)

(b)

Figure 1: An image of a nevus surrounded by a line of a fishing rod in (a) and
its evolution after two months of treatment in (b).

###  2.3 Post-visit Perception

Patient follow-ups can be almost as important as the actual medical visit,
given that they help ensure patients have adhered to their treatments and in
tracking the treatments’ effects. Given the observed historical tendency for
patients to potentially neglect in-person follow-ups, digital tools have shown
utility in enabling convenient remote check-ins for post-visit care and
tracking. For example, the patient can use digital tools to send images of the
progress of their burn to their dermatologist, allowing for remote monitoring
as well as treatment adjustments if necessary. A distinct phenomenon observed
in Tunisia is related to the differences between the prescribed treatment by
dermatologists (and doctors in general) and the one ultimately provided by
pharmacists. These differences can be due to the lack of availability of
specific drugs or brands in pharmacies, which is a simple and logical
justification. However, the most common reason for the difference in what is
prescribed is explained by the unfortunate unfolding competition between
pharmacists, who can withhold patients’ prescriptions until the unavailable
drugs are in-stock again, even though the specifically needed drugs are
already available in other pharmacies. In such situations, the ideal patient
contacts the dermatologist to verify if the drugs they obtained from the
pharmacist are equivalent to the ones prescribed. However, due to the lack of
sensitivity and awareness in most cases, the patients will start their
treatment without giving adequate attention to the changes in the prescription
made by their pharmacists. Given these more nuanced and technical problems,
which are seldom present in more developed countries, dermatologists in
Tunisia often find themselves playing the role of “medical policeman”. For
example, the dermatologist can instruct their patient to take a photo of their
treatment before beginning to use it to allow for the dermatologist to confirm
what has been issued by the pharmacist. A similar issue with cosmetic products
that has become more prevalent in recent years is when a patient exchanges
drugs with friends, especially when dealing with budget limitations, thereby
neglecting the specific prescribed dosage of drugs from the dermatologist for
a personalized and optimal therapeutic result. From this perspective, the
perceived role of dermatologists revolves around checking treatments provided
by pharmacists and answering any questions, which is often expected for free
in cases of complications and uncertainty.

##  3 The Historical Evolution of Digital Tools & Their Patient-Behavior
Impact Across Clinical Stages

The adoption of digital tools has evolved significantly over the past ten
years, with distinct progress impacting patient behavior as compared to the 25
years prior to that. However, the full potential of digital tools in the
healthcare industry is still far from being realized, with historical
implementations largely centering around broader communication and information
accessing platforms. While this improves patients’ access to any kind of
relevant medical information and provides the opportunity to discuss with
fellow patients, these tools can be very high in risk of mis-education or mis-
treatment, and can hinder dermatologists’ efforts in providing optimal care.
We investigate “Dr. Google”, social media, and AI tools, the three major
digital tools that were used over the last 10 years in Tunisia, and assess
their impact across the clinical stages.

###  3.1 Social Media

The usage of social media in Tunisia, in particular Facebook, has undergone an
astonishing growth over the last ten years, and today, Facebook drives 92.65%
of Tunisian social media traffic in the month of June 2022, with similar
levels over the last 12 months; Facebook’s dominance significantly beats other
platforms in that category which includes YouTube, Twitter, Instagram,
Pinterest, LinkedIn, Reddit, and other platforms [1]. Popular applications
such as Facebook, Messenger, and Instagram are freely and instantly accessible
and provide user-friendly platforms to search and share medical information.
Further, Tunisian patients also rely on Facebook groups and pages to seek
other patients’ opinions and ratings of different dermatologists or clinics.
Such platforms offer support systems where disease-focused groups or
communities can be created to share experiences and provide psychological
support. Facebook and Messenger usage is not restricted to patients,
dermatologists also utilize such tools to discuss and solicit each other’s
opinions and potential diagnoses on more complex or ambiguous cases [9].
Furthermore, social media, and messaging apps facilitate useful patient-doctor
and doctor-doctor communications. If the doctor has an online presence, such
tools can be used to provide clinic communications, virtual assistance and
facilitate follow-ups. Social media platforms have been generally used by
Tunisian dermatologists to raise awareness, encourage preventive or misguided
behaviors, and offer advice and information on skin diseases to help minimize
the risks of self-education and self-treatment. However, the availability of
dermatologists to directly regulate content on such platforms is minimal, and
their impact is unscalable given the broad usage of those platforms by the
general population.

Unfortunately, social media is not always a reliable source of information,
especially with complex and highly-personalized diagnoses and treatments, and
has been shown to quite easily and quickly disseminate false or misleading
information to widespread audiences. Such platforms are often used by
commercial organizations or sales-representatives seeking to aggressively
promote products for profit, which can lead to harmful misinformation or
consequences [14].

###  3.2 “Dr. Google”

Search engine market share in Tunisia is heavily dominated by Google, with
94.95% of the total search engine market as of June 2022 [1]. With around 72%
of residents having access to the internet, and with over 90% of the
population having at least one mobile phone, accessibility to the internet and
information is quite high, enabling most patients to consult the internet
before visiting a dermatologist. Patients usually seek information through
searching their symptoms or using more advanced search tools like reverse
image searches. The latter is a software tool that enables users to find
visually similar images simply by uploading a photograph of the skin lesion.
While these tools are used in both developed and underdeveloped countries,
dermatologists in Tunisia have emphasized that patients in underdeveloped or
economically challenged regions, such as Tunisia, have been driven to rely
heavily on these online tools to solely self-medicate, deferring a consult
with a specialist to when symptoms significantly deteriorate or misalign with
their self-diagnosis.

The ability for patients to seek medical information through various online
search engines outside their clinical visit can be beneficial but can also
pose significant risks. Self-educating through online resources can facilitate
more comprehensive communication between patients and dermatologists,
encourage the patient to ask more questions, alert the dermatologist to
relevant aspects of their case, and become more engaged with their treatment
across the clinical stages. However, the vast amount of information online can
be overwhelming, and the ability to ascertain an accurate online diagnosis is
low and dangerous, yet is unfortunately still relied upon by patients in
deferring visits, especially in less developed or economically challenged
countries.

###  3.3 The first AI tools

AI is playing a pivotal role in advancing e-health solutions for dermatology
[8, 11, 2]. Dermatology is a suitable high-value use case for AI, as it is a
visual-dependent specialty and given that numerous image recognition
algorithms have already been proven to be effective in detecting skin diseases
and cancers [6]. This progress in AI has also fueled the design of several web
or mobile image-based diagnosis apps for skin conditions such as “AIPDerm”,
which has had significant success in Europe. These AI-based tools are
beneficial for various expertise levels within a clinic, from senior
dermatologists to junior nurses, and help distinguish possible skin conditions
and provide more sophisticated differential diagnosis possibilities along with
ancillary information. AI-based diagnostic tools also play an important role
in teledermatology given that they can help patients avoid long waiting
periods and pre-identify and triage the severity of their skin condition.

Currently, most dermatologists in Tunisia do not use AI tools for a second
opinion. We asked more than 300 dermatologists about their reasons for not yet
adopting AI tools, and more than 96% were worried about the following two
aspects of these applications:

  * •

Privacy concerns about users’ health data: most applications indicate they
would not use uploaded images to target advertising, and would only save them
to further train their classification algorithms, if users gave them explicit
permission to do so. However, dermatologists in Tunisia do not have a legal
framework established for digital dermatology, and at this early juncture,
still feel insecure about adopting these applications.

  * •

Limited coverage of skin diseases: most dermatology AI applications are
limited in scope of capabilities as they are narrowly targeted toward specific
skin diseases such as skin cancers. In other words, they do not cover a wide
enough diagnostic capability supporting the majority of skin diseases and
cancers.

Remote triaging solutions based on AI tools offer the potential for addressing
challenges in many low- and middle-income countries in Africa, or similar
economic and culturally inhibiting regions, especially if offered at scale
[12]. However, the need for in-person visits remains irreplaceable in many
cases, and where the need for precise and nuanced knowledge of medical context
is critical. Fig. 2 shows one of those cases where the back of the patient has
well-defined, painless but itchy, multiple lesions, such as scratches,
ulcerations, or cuts. The patient uses a brush to scratch her back. All AI
applications we have tested failed this case as it looks like skin picking.
This is because the correct diagnosis, pathomimia (a.k.a., factitious
dermatosis), is self-induced and often difficult to find if the dermatologist
does not consider the psychological profile of the patient, which is confirmed
by the opinion of the psychiatrist.

Figure 2: The back of a patient with pathomimia disease triggered by a
psychological concern.

##  4 The Future: How Can Advanced Digital Tools Optimize Patient Behavior
Across All Clinical Stages and Address Economic & Cultural Barriers?

Although we have witnessed transformative evolutions in digital connectivity
tools over the last few decades, healthcare remains stagnated in a world of
under-adoption and misutilization. The available tools are constantly evolving
to be far more sophisticated and tailored than the basic platforms broadly
used today. We have begun to see the potential of these future tools in
addressing the weakness and limitations of past tools, as global efforts in
developing dermatology AI tools have been substantial. With respect to the
future capabilities of AI systems, we believe its core features should address
the patient care and digital tool issues described in this paper. One of the
issues is dangerous self-education, and the vast degree of error and risk
associated with that. With a system that utilizes sophisticated diagnosis AI
modules such as a visual image diagnosis system, the system can minimize the
associated error risk by providing a highly accurate diagnostic tool that is
not at the mercy of patient diligence. It can be accessed by patients, with
results sent to the clinic for oversight, and used as a high-confidence
triaging and education tool benefiting both the pre-visit and in-visit stages.
For the post-visit stage, a patient management platform can offer a patient
portal that provides direct information about their diagnosis and treatments
along with detailed reference material for contextual explanations, minimizing
the possibility of speculation, skepticism, and non-adherence. This platform
can also provide the ability for patients to asynchronously submit questions
to the clinic regarding any concerns, minimizing misinformation and ensuring
patient actions and understandings are carefully managed by the clinic. This
system will also minimize patients’ delays in addressing a possible ailment
due to economic reasons and false confidence from self-education, by instantly
providing a sophisticated triage and education tool at no incremental overhead
costs due to its scalability. The system will also allow dermatologists and
their staff to focus their efforts on management and oversight of a single
platform while allowing for the ability to follow up with patients on
obtaining correct prescriptions and subsequent treatment adherence, as well as
enable remote condition monitoring post-visit, which more efficiently
qualifies the need for future in-person visits. Such a system has already been
comprehensively developed, clinically validated, and successfully deployed at
a country-wide level in Europe – “AIPDerm’, which is based in Hungary and
Spain.

Economically, in regions where fiscal stability is sensitive, we believe the
scalability and accessibility of such platforms can be transformative.
Culturally, we believe such a system is especially relevant in regions such as
Tunisia, where longstanding cultural norms and sensitivities can inhibit the
requisite treatment for many. In such regions, this can manifest within
households where female members can be prohibited from seeing a physician by
the male patriarch of the family on the basis of privacy, protection, and
cultural sensitivities. We believe that the system can help mitigate such
issues by allowing patients to localize the lesion in an image, minimize the
inclusion of other body parts, and not require the patient’s physical
presence, allowing for remote triaging of their case. Overall, we believe this
envisioned system can largely solve the aforementioned patient behavior
issues, including cultural and economic constraints, and reduce the perversion
of existing basic tools and social media platforms.

##  5 Conclusion

Patient behavior in dermatology has undergone significant changes over the
past 35 years, predominantly over the last decade, driven by significant
transformation and adoption of digital tools. Patient behavior is analyzed
across three clinical stages: pre-visit, in-visit, and post-visit, and is
classified into several profiles that reflect observed patient behaviors. The
causes of different patients’ behaviors are not singular and this work
illuminates the predominantly witnessed patterns in Tunisia. Digital tools
have materially impacted how patients behave in all three clinical stages,
with various economic and cultural aspects specific to Tunisia also
contributing to the patterned patient behavior. While basic digital tools have
exposed patients to risks of self-education and self-medication, there is
clear potential in minimizing those risks and in mitigating the economic and
cultural concerns of Tunisia and various regions globally. The aim of this
paper was to describe patient behavior over 35 years in Tunisia across the
three clinical stages, while analyzing the coinciding effects of economic and
cultural manifestations, and the overarching impact of digital tools. We
culminated by detailing how digital tools can mitigate Tunisia’s, and similar
regions’, economic and cultural constraints, and how specific innovations can
significantly evolve the current standard of how digital tools impact patient
behaviors and outcomes.

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