Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning. In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.   Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward. Science requires reproducibility, but many sub-fields of science have recently experienced a reproducibility crisis, eroding trust in processes and results and potentially influencing the rising rates of scientific retractions [1, BID4 BID43 . Reproducibility is also critical for machine learning research, whose goal is to develop algorithms to reliably solve complex tasks at scale, with limited or no human supervision. Failure of a machine learning system to consistently replicate an intended behavior in a context different from which that behavior was defined may result in dramatic, even fatal, consequences BID26 . Ranking prominently among machine learning applications that may put human lives at stake are those related to Machine Learning for Health (ML4H). In a field where applications are meant to directly affect human health, findings should undergo heavy scrutiny along the validation pipeline from research findings to applications deployed in the wild. For example, in 2018, 12 AI tools using ML4H algorithms to inform medical diagnosis and treatment were cleared by Food and Drug Administration (FDA) and will be marketed to and potentially used by millions of Americans BID30 . Verifying the reproducibility of the claims put forward by the device manufacturer should thus be a main priority of regulatory bodies BID35 , extending the need for reproducible ML4H results beyond the machine learning research community.Unfortunately, several factors relating to the availability, quality, and consistency of clinical or biomedical data make reproducibility especially challenging in ML4H applications. In this work, * Equal Contribution we make several contributions. First, we present a taxonomy of reproducibility tailored to ML4H applications, and designed to capture reproducibility goals more broadly. Second, we use this taxonomy to define several metrics geared towards quantifying the particular challenges in reproducibility faced within ML4H, and conduct a comprehensive review of the published literature to support our claims and compare ML4H to machine learning more generally. Finally, we build on this analysis by exploring promising areas of further research for reproducibility in ML4H. In this work, we have framed the question of reproducibility in ML4H around three foundational lenses: technical, statistical, and conceptual replicability. In each of these areas, we argue both qualitatively and quantitatively, through a manual, extensive review of the literature, that ML4H performs worse than other machine learning fields in several reproducibility metrics we have identified. While keeping in mind the intrinsic challenges of data acquisition and use that plague the field, we highlight several areas of opportunities for the future, focused around improving access to data, expanding our trajectory of statistical rigor, and increasing the use of multi-source data to better enable conceptual reproducibility. * indicates all publiclyaccessible papers published were used.Potential Biases This selection and annotation procedure allowed us to analyze a large number of papers, but has several possible biases. In particular, our annotation questions were all of these were designed to be determinable via quick, scanning techniques and as a result this task took on average between 45 seconds and 3 minutes per paper. In such a limited time, some losses are unavoidable. We recognize several sources of possible bias worth mentioning.Firstly, some papers may, for example, release datasets or code products external to the paper and not mention it in the actual text. We will omit these associated products. If such effects induce a notable bias in our results, however, we must question why as a field we are comfortable releasing our code/data without any mention in the associated paper.Secondly, not all papers intended to be analyzed were publicly accessible. Similarly, the versions of papers we analyzed could have been different from the version presented at the actual conference venue, or there could exist updated versions of papers we analyzed in different repositories. Our analysis technique will miss these effects.Thirdly, some papers naturally fit into multiple categories (e.g., a work focused on medical named entity recognition would be both a ML4H work and an NLP work). In the interest of ensuring our comparison classes were as pure as possible, we omitted all clearly multi-domain works, but allowed works that centered primarily in a single domain to remain.Lastly, different fields present different kinds of works, and not all works fit into our framework. Largely theoretical works, for example, often have no real datasets or public experiments. Similarly, presenting variance is a different question for works focused principally around computational efficiency rather than predictive accuracy. We handled these issues by attempting to answer these questions as best we could, and flagging any papers that overtly did not fit our scheme and excluding them from our analyses.
By analyzing more than 300 papers in recent machine learning conferences, we found that Machine Learning for Health (ML4H) applications lag behind other machine learning fields in terms of reproducibility metrics. This paper conducts a quantitative and qualitative review of the state of the reproducibility for ML healthcare applications and proposes reccomendations to make research more reproducible.