Extensions of the algorithm to learning non-linear metrics via kernels , and as a pre-processing step for dimensionality reduction are discussed . The new method is demonstrated on a regression problem ( structure-based chemical shift prediction ) and a classification problem ( predicting clinical outcomes for immunomodulatory strategies for treating severe sepsis ) . Image-based classification of histology sections plays an important role in predicting clinical outcomes . However this task is very challenging due to the presence of large technical variations ( e.g. , fixation , staining ) and biological heterogeneities ( e.g. , cell type , cell state ) . Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy , such as after an Achilles Tendon Rupture ( ATR ) . We formulate the problem of data imputation and prediction for ATR relevant medical measurements into a recommender system framework . Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics . Existing models provide the patient with pragmatic ( supervised ) predictions of risk , but do not provide the clinician with intelligible ( unsupervised ) representations of disease pathophysiology . We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality . Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes . The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes , such as survival and disease recurrence . Such methods are also important to better understand the biological mechanisms underlying disease etiology and development , as well as treatment responses . It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets . In addition , we provide several case studies to illustrate pathwayPCA analysis with gene selection , estimating and visualizing sample-specific pathway activities , identifying sex-specific pathway effects in kidney cancer , and building integrative models for predicting patient prognosis . pathwayPCA is an open-source R package , freely available through the Bioconductor repository . More tests made with higher prediction periods demonstrate a slight decrease in the performance . Simulation results demonstrate the effectiveness of the FNARX model proposed , which has a very good performance in predicting patient conditions , and it is a useful tool for preventive medicine . To achieve this , we introduce novel image features for this task that when combined with standard machine learning classifiers yield excellent treatment outcome classification capabilities . ResultsOur approach was evaluated on both ex vivo porcine eyes and human patients in a clinical setting , yielding performances above 95 % accuracy for predicting patient treatment outcomes . In addition , we show that accurate outcomes for human patients can be estimated even when our method is trained using only ex vivo porcine data . One reason is the complex relationships between biomarkers and the patient 's response to drugs , obscuring the true weight of the biomarkers in the overall patient 's response . This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions , for predicting the clinical outcomes . Several computational tools have been developed for personalized oncology , notably evidence-based tools for simulating pharmacokinetics , Bayesian-estimated tools for predicting survival , etc . He has been spending the past decade researching and developing models to better understand behaviours and patterns that relate real world human activities . Modeling of time series health data using Dynamic Bayesian Networks : An application to predictions of patient outcomes after multiple surgeries Xiongcai Cai , Oscar Perez-Concha , Fernando Martin-Sanchez , Blanca Gallego a Centre for Health Informatics , Australian Institute of Health Innovation , University of New South Wales , NSW 2052 b Health and Biomedical Informatics Centre , University of Melbourne , Victoria 3010 SUMMARY Objective : To develop dynamic predictive models for real-time outcome predictions of hospitalised patients . section : Introduction Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologies and are important for planning of treatment strategies , monitoring of disease progression and prediction of patient outcome . For a better understanding of the pathophysiology of diseases , quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures . Conclusions This study demonstrates that FAST is accurate on operations . FAST is provided by the integrated radiologist as part of damage control radiology , which gives the team leader rapid diagnostic information to improve decision-making and ultimately patient outcomes . CT is heavily utilised in civilian practice ; however , the military operates in a different environment often with multiple casualties and limited access to CT , as a result , portable ultrasound will continue to be a valuable tool on operations if used properly . There is much debate on which types of computer-based systems have the most impact in healthcare delivery and patient outcomes . Safety surveillance systems , which have been around for several years , are probably at the top of the list . The statistical literature demonstrates that greedy/myopic algorithms are less than optimal under most circumstances and will be optimal in only special cases . Furthermore , many current methods do not formally consider respondent burden , which can be an important consideration when computerized adaptive tests are used to measure patient-reported outcomes . A fully Bayesian adaptive sequential design optimizes over all possibilities and mathematically accounts for the possibility of asking subsequent items beyond the next one . Specifically , showing clinical benefit is essential for the adoption of ECGI . Such benefit may lie in patient outcome improvement , workflow improvement , or cost reduction . Future studies should focus on these aspects to achieve broad adoption of ECGI , but only after the technical challenges have been solved for that specific application/pathology . From the computational perspective , heuristic-based test scheduling might lead to reduced accuracy of clinical forecasting models . Computationally learning an optimal clinical test scheduling and measurement collection , is likely to lead to both , better predictive models and patient outcome improvement . We address the scheduling problem using deep reinforcement learning ( RL ) to achieve high predictive gain and low measurement cost , by scheduling fewer , but strategically timed tests . The new value-centric care model has the ability to deliver high quality care through rigorous basic science and technological applications . To name a few , 3D printing of orthotics , advanced technology for surgery via arthroplasty , robotics , patient care outcome measures and biomechanical intervention based physical therapy are some of the hot topics in present day orthopedics . In this inaugural issue of Research and Reviews : Orthopedics , we aim to foster rapid peer-reviewed articles that are directed to orthopedic research . The Future of Nursing report 1 underscores the belief that nurses can play a vital role in patient care outcomes . Among many things , the report discusses nurses practicing to the fullest extent of their education , nurse/physician collaboration , and team approaches . RR-APET scored an overall usability rating of 4.16 out of a possible 5 , when released on a trial basis for user evaluation . CONCLUSIONS With its unique ability to both create and operate on large databases , this software provides a strong platform from which to conduct further research in the field of HRV analytics and its correlation to patient healthcare outcomes . This software is available free of charge at https://gitlab.com/MegMcC/rr-apet-hrv-analysis-software and can be operated as an executable file within Windows , Mac and Linux systems . Background Health system decisions to put new technologies into clinical practice require a rapid and trustworthy decision-making process informed by best evidence . Objective This study aimed to present a rapid evidence review process that can be used to inform health system leaders and clinicians seeking to implement new technology tools to improve patient-clinician decision making and patient-oriented outcomes . Methods The rapid evidence review process we pioneered involved 5 sequential subprocesses : ( 1 ) environmental scan , ( 2 ) expert panel recruitment , ( 3 ) host evidence review panel , ( 4 ) analysis , and ( 5 ) local validation panel . This study describes how Texas Children 's Hospital customized a commercial EHR system and assembled a cross-functional team to capture outcomes data using existing functionality . Prior to its installation and customization , no surgical subspecialties besides the congenital heart and transplant surgery groups conducted prospective , patient outcomes measurement , but by 2015 , the outcomes of over 1300 unique patients with supracondylar fractures , cleft lip and/or palate , or voiding dysfunction had been tracked . Key factors for integrating outcomes measurement into the clinical workflow include ongoing communication between cross-functional teams composed of clinicians and technical professionals , an iterative design process , organizational commitment , and prioritizing measurement as early as possible during EHR optimization . Quality of life is a complex concept , entailing the person 's physical health and level of independence , as well as psychological well-being , social participation and the relationship with specific and relevant aspects of the environment . The World Health Organization encourages the integration of a comprehensive definition of health when examining patient-related outcomes after injury or disease . This study aims at evaluating the quality of life of 130 Italian patients with spinal cord injury , focusing on the associations among functional status and health dimensions elicited by SF-36 questionnaire . It is thus practically impossible for physicians to know which is the best medical intervention for a given patient group and condition . This inability to easily search and organize the published literature impedes the aims of evidence based medicine ( EBM ) , which aspires to inform patient care using the totality of relevant evidence . Computational methods could expedite biomedical evidence synthesis and natural language processing ( NLP ) in particular can play a key role in the task . Prediction of overall survival based on multimodal MRI of brain tumor patients is a difficult problem . Although survival also depends on factors that can not be assessed via preoperative MRI such as surgical outcome , encouraging results for MRI-based survival analysis have been published for different datasets . Second , we propose to fuse the unimodal predictions trained on each training subject before performing the multimodal fusion . The results show that our high-level features improve the performance of the multimodal prediction of arousal and that the subjects fusion works well in unimodal prediction but generalizes poorly in multimodal prediction , particularly on valence . Our model builds on recent advances in neural network generative modeling and scalable inference , and uses hierarchical structure to learn editing patterns across many diverse users . Empirically , we find that our model outperforms other approaches on this challenging multimodal prediction task . We first describe our surface acquisition system and introduce a set of clinical measurements ( indices ) based on the trunk 's external shape , to quantify its degree of asymmetry . We then describe our 3D reconstruction system of the spine and rib cage from biplanar radiographs and present our methodology for multimodal fusion of MRI , X-ray and external surface images of the trunk We finally present a physical model of the human trunk including bone and soft tissue for the simulation of the surgical outcome on the external trunk shape in AIS . Based on the evaluations and results obtained in the quantitative empirical research , a method for multimodal integration patterns modeling with utilization of machine learning algorithms , namely Bayesian Networks , is designed and proposed in the following part of the thesis . The constructed probability model is capable of very precise and robust multimodal input prediction with accuracy of 99 % . A procedure for applying the predictive capabilities of the constructed classification model to address the multimodal input segmentation is then introduced . The advantage of this method lies in that we combined multimodal MRI information through CNN convolution kernel , and achieved a higher classification accuracy . In our experiments , the comprehensive classification accuracy of AD , aMCI patients and NC was as high as 92.06 % when using multimodal MRI data as input , which is effective enough to provide a reference for multimodal MRI data analysis . We evaluate DCMN models on two outcome prediction tasks , including a mortality prediction on the public Medical Information Mart for Intensive Care III ( MIMIC-III ) database and a cost prediction on the Hospital Quality Monitoring System ( HQMS ) dataset . Experimental results demonstrate that our DCMN models are more competitive over the baseline methods in the multimodal prediction setting . In our previous work , we showed that fractal-based texture features are effective in detection , segmentation and classification of posterior-fossa ( PF ) pediatric brain tumor in multimodality MRI . We exploited an information theoretic approach such as Kullback-Leibler Divergence ( KLD ) for feature selection and ranking different texture features .
Extensions of the algorithm to learning non-linear metrics via kernels , and as a pre-processing step for dimensionality reduction are discussed . The new method is demonstrated on a regression problem ( structure-based chemical shift prediction ) and a classification problem ( predicting clinical outcomes for immunomodulatory strategies for treating severe sepsis ) .
Image-based classification of histology sections plays an important role in predicting clinical outcomes . However this task is very challenging due to the presence of large technical variations ( e.g. , fixation , staining ) and biological heterogeneities ( e.g. , cell type , cell state ) .
Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy , such as after an Achilles Tendon Rupture ( ATR ) . We formulate the problem of data imputation and prediction for ATR relevant medical measurements into a recommender system framework .
Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics . Existing models provide the patient with pragmatic ( supervised ) predictions of risk , but do not provide the clinician with intelligible ( unsupervised ) representations of disease pathophysiology .
We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality . Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes .
The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes , such as survival and disease recurrence . Such methods are also important to better understand the biological mechanisms underlying disease etiology and development , as well as treatment responses .
It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets . In addition , we provide several case studies to illustrate pathwayPCA analysis with gene selection , estimating and visualizing sample-specific pathway activities , identifying sex-specific pathway effects in kidney cancer , and building integrative models for predicting patient prognosis . pathwayPCA is an open-source R package , freely available through the Bioconductor repository .
More tests made with higher prediction periods demonstrate a slight decrease in the performance . Simulation results demonstrate the effectiveness of the FNARX model proposed , which has a very good performance in predicting patient conditions , and it is a useful tool for preventive medicine .
To achieve this , we introduce novel image features for this task that when combined with standard machine learning classifiers yield excellent treatment outcome classification capabilities . ResultsOur approach was evaluated on both ex vivo porcine eyes and human patients in a clinical setting , yielding performances above 95 % accuracy for predicting patient treatment outcomes . In addition , we show that accurate outcomes for human patients can be estimated even when our method is trained using only ex vivo porcine data .
One reason is the complex relationships between biomarkers and the patient 's response to drugs , obscuring the true weight of the biomarkers in the overall patient 's response . This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions , for predicting the clinical outcomes . Several computational tools have been developed for personalized oncology , notably evidence-based tools for simulating pharmacokinetics , Bayesian-estimated tools for predicting survival , etc .
He has been spending the past decade researching and developing models to better understand behaviours and patterns that relate real world human activities . Modeling of time series health data using Dynamic Bayesian Networks : An application to predictions of patient outcomes after multiple surgeries Xiongcai Cai , Oscar Perez-Concha , Fernando Martin-Sanchez , Blanca Gallego a Centre for Health Informatics , Australian Institute of Health Innovation , University of New South Wales , NSW 2052 b Health and Biomedical Informatics Centre , University of Melbourne , Victoria 3010 SUMMARY Objective : To develop dynamic predictive models for real-time outcome predictions of hospitalised patients .
section : Introduction Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologies and are important for planning of treatment strategies , monitoring of disease progression and prediction of patient outcome . For a better understanding of the pathophysiology of diseases , quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures .
Conclusions This study demonstrates that FAST is accurate on operations . FAST is provided by the integrated radiologist as part of damage control radiology , which gives the team leader rapid diagnostic information to improve decision-making and ultimately patient outcomes . CT is heavily utilised in civilian practice ; however , the military operates in a different environment often with multiple casualties and limited access to CT , as a result , portable ultrasound will continue to be a valuable tool on operations if used properly .
There is much debate on which types of computer-based systems have the most impact in healthcare delivery and patient outcomes . Safety surveillance systems , which have been around for several years , are probably at the top of the list .
The statistical literature demonstrates that greedy/myopic algorithms are less than optimal under most circumstances and will be optimal in only special cases . Furthermore , many current methods do not formally consider respondent burden , which can be an important consideration when computerized adaptive tests are used to measure patient-reported outcomes . A fully Bayesian adaptive sequential design optimizes over all possibilities and mathematically accounts for the possibility of asking subsequent items beyond the next one .
Specifically , showing clinical benefit is essential for the adoption of ECGI . Such benefit may lie in patient outcome improvement , workflow improvement , or cost reduction . Future studies should focus on these aspects to achieve broad adoption of ECGI , but only after the technical challenges have been solved for that specific application/pathology .
From the computational perspective , heuristic-based test scheduling might lead to reduced accuracy of clinical forecasting models . Computationally learning an optimal clinical test scheduling and measurement collection , is likely to lead to both , better predictive models and patient outcome improvement . We address the scheduling problem using deep reinforcement learning ( RL ) to achieve high predictive gain and low measurement cost , by scheduling fewer , but strategically timed tests .
The new value-centric care model has the ability to deliver high quality care through rigorous basic science and technological applications . To name a few , 3D printing of orthotics , advanced technology for surgery via arthroplasty , robotics , patient care outcome measures and biomechanical intervention based physical therapy are some of the hot topics in present day orthopedics . In this inaugural issue of Research and Reviews : Orthopedics , we aim to foster rapid peer-reviewed articles that are directed to orthopedic research .
The Future of Nursing report 1 underscores the belief that nurses can play a vital role in patient care outcomes . Among many things , the report discusses nurses practicing to the fullest extent of their education , nurse/physician collaboration , and team approaches .
RR-APET scored an overall usability rating of 4.16 out of a possible 5 , when released on a trial basis for user evaluation . CONCLUSIONS With its unique ability to both create and operate on large databases , this software provides a strong platform from which to conduct further research in the field of HRV analytics and its correlation to patient healthcare outcomes . This software is available free of charge at https://gitlab.com/MegMcC/rr-apet-hrv-analysis-software and can be operated as an executable file within Windows , Mac and Linux systems .
Background Health system decisions to put new technologies into clinical practice require a rapid and trustworthy decision-making process informed by best evidence . Objective This study aimed to present a rapid evidence review process that can be used to inform health system leaders and clinicians seeking to implement new technology tools to improve patient-clinician decision making and patient-oriented outcomes . Methods The rapid evidence review process we pioneered involved 5 sequential subprocesses : ( 1 ) environmental scan , ( 2 ) expert panel recruitment , ( 3 ) host evidence review panel , ( 4 ) analysis , and ( 5 ) local validation panel .
This study describes how Texas Children 's Hospital customized a commercial EHR system and assembled a cross-functional team to capture outcomes data using existing functionality . Prior to its installation and customization , no surgical subspecialties besides the congenital heart and transplant surgery groups conducted prospective , patient outcomes measurement , but by 2015 , the outcomes of over 1300 unique patients with supracondylar fractures , cleft lip and/or palate , or voiding dysfunction had been tracked . Key factors for integrating outcomes measurement into the clinical workflow include ongoing communication between cross-functional teams composed of clinicians and technical professionals , an iterative design process , organizational commitment , and prioritizing measurement as early as possible during EHR optimization .
Quality of life is a complex concept , entailing the person 's physical health and level of independence , as well as psychological well-being , social participation and the relationship with specific and relevant aspects of the environment . The World Health Organization encourages the integration of a comprehensive definition of health when examining patient-related outcomes after injury or disease . This study aims at evaluating the quality of life of 130 Italian patients with spinal cord injury , focusing on the associations among functional status and health dimensions elicited by SF-36 questionnaire .
It is thus practically impossible for physicians to know which is the best medical intervention for a given patient group and condition . This inability to easily search and organize the published literature impedes the aims of evidence based medicine ( EBM ) , which aspires to inform patient care using the totality of relevant evidence . Computational methods could expedite biomedical evidence synthesis and natural language processing ( NLP ) in particular can play a key role in the task .
Prediction of overall survival based on multimodal MRI of brain tumor patients is a difficult problem . Although survival also depends on factors that can not be assessed via preoperative MRI such as surgical outcome , encouraging results for MRI-based survival analysis have been published for different datasets .
Second , we propose to fuse the unimodal predictions trained on each training subject before performing the multimodal fusion . The results show that our high-level features improve the performance of the multimodal prediction of arousal and that the subjects fusion works well in unimodal prediction but generalizes poorly in multimodal prediction , particularly on valence .
Our model builds on recent advances in neural network generative modeling and scalable inference , and uses hierarchical structure to learn editing patterns across many diverse users . Empirically , we find that our model outperforms other approaches on this challenging multimodal prediction task .
We first describe our surface acquisition system and introduce a set of clinical measurements ( indices ) based on the trunk 's external shape , to quantify its degree of asymmetry . We then describe our 3D reconstruction system of the spine and rib cage from biplanar radiographs and present our methodology for multimodal fusion of MRI , X-ray and external surface images of the trunk We finally present a physical model of the human trunk including bone and soft tissue for the simulation of the surgical outcome on the external trunk shape in AIS .
Based on the evaluations and results obtained in the quantitative empirical research , a method for multimodal integration patterns modeling with utilization of machine learning algorithms , namely Bayesian Networks , is designed and proposed in the following part of the thesis . The constructed probability model is capable of very precise and robust multimodal input prediction with accuracy of 99 % . A procedure for applying the predictive capabilities of the constructed classification model to address the multimodal input segmentation is then introduced .
The advantage of this method lies in that we combined multimodal MRI information through CNN convolution kernel , and achieved a higher classification accuracy . In our experiments , the comprehensive classification accuracy of AD , aMCI patients and NC was as high as 92.06 % when using multimodal MRI data as input , which is effective enough to provide a reference for multimodal MRI data analysis .
We evaluate DCMN models on two outcome prediction tasks , including a mortality prediction on the public Medical Information Mart for Intensive Care III ( MIMIC-III ) database and a cost prediction on the Hospital Quality Monitoring System ( HQMS ) dataset . Experimental results demonstrate that our DCMN models are more competitive over the baseline methods in the multimodal prediction setting .
In our previous work , we showed that fractal-based texture features are effective in detection , segmentation and classification of posterior-fossa ( PF ) pediatric brain tumor in multimodality MRI . We exploited an information theoretic approach such as Kullback-Leibler Divergence ( KLD ) for feature selection and ranking different texture features .
A Randomized Algorithm for Learning Mahalanobis Metrics: Application to Classification and Regression of Biological Data Classification of Histology Sections via Multispectral Convolutional Sparse Coding Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation Attentive State-Space Modeling of Disease Progression A deep neural network approach to predicting clinical outcomes of neuroblastoma patients A deep neural network approach to predicting clinical outcomes of neuroblastoma patients PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data. Dynamic Fuzzy-Neural based tool formonitoring and predicting patients conditions using selected vital signs Automatic assessment of time-resolved OCT images for selective retina therapy Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration. Modeling of time series health data using dynamic Bayesian networks: an application to predictions of patient outcomes after multiple surgeries Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation FAST in the deployed military setting Computer-based safety surveillance and patient-centered health records Bayesian Adaptive Sequential Item Selection (BASIS): An Opmtimal Approach to Item Selection Validation and Opportunities of Electrocardiographic Imaging: From Technical Achievements to Clinical Applications Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL Current Trends in Orthopedic Research Onboarding Nurse Practitioners: A Healthcare System Approach to Interprofessional Education RR-APET - Heart rate variability analysis software A Rapid Process for Identifying and Prioritizing Technology-Based Tools for Health System Implementation Incorporating longitudinal pediatric patient-centered outcome measurement into the clinical workflow using a commercial electronic health record: a step toward increasing value for the patient Quality of life of patients with spinal cord injury in Italy: preliminary evaluation A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature Robustness of Radiomics for Survival Prediction of Brain Tumor Patients Depending on Resection Status High-Level Geometry-based Features of Video Modality for Emotion Prediction Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models Multimodal image fusion of anatomical structures for diagnosis, therapy planning and assistance Response Time Improvement of Multimodal Interactive Systems A Novel Multimodal MRI Analysis for Alzheimer's Disease Based on Convolutional Neural Network DCMN: Double Core Memory Network for Patient Outcome Prediction with Multimodal Data Multiclass feature selection for improved pediatric brain tumor segmentation
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2005 2014 2016 2019 2019 2019 2020 2015 2016 2014 2014 2017 2018 2012 2009 2018 2018 2017 2012 2020 2018 2016 2014 2018 2019 2016 2017 2013 2017 2018 2019 2012
APBC CVPR NeurIPS Medical Image Anal. MIE ACL AVEC@ACM Multimedia EMBC ICDM Medical Imaging: Computer-Aided Diagnosis
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