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Register a new userDeploying clinical machine learning? Consider the following...
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Added by
Charles Lu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Despite the intense attention and investment into clinical machine learning (CML) research, relatively few applications convert to clinical practice. While research is important in advancing the state-of-the-art, translation is equally important in bringing these technologies into a position to ultimately impact patient care and live up to extensive expectations surrounding AI in healthcare. To better characterize a holistic perspective among researchers and practitioners, we survey several participants with experience in developing CML for clinical deployment about their learned experiences. We collate these insights and identify several main categories of barriers and pitfalls in order to better design and develop clinical machine learning applications.
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Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do not account for dates and limit the generalisability of date-agnostic models. In this work, we establish the magnitude of this problem on MIMIC, a public hospital dataset, and showcase a simple solution. We augment MIMIC with the year in which care was provided and show that a model trained using standard feature representations will significantly degrade in quality over time. We find a deterioration of 0.3 AUC when evaluating mortality prediction on data from 10 years later. We find a similar deterioration of 0.15 AUC for length-of-stay. In contrast, we demonstrate that clinically-oriented aggregates of raw features significantly mitigate future deterioration. Our suggested aggregated representations, when retrained yearly, have prediction quality comparable to year-agnostic models.
In recent years, machine learning has received increased interest both as an academic research field and as a solution for real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. Our survey shows that practitioners face challenges at each stage of the deployment. The goal of this paper is to layout a research agenda to explore approaches addressing these challenges.
The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities. Several recent works frame the problem as that of algorithmic fairness, a framework that has attracted considerable attention and criticism. However, the appropriateness of this framework is unclear due to both ethical as well as technical considerations, the latter of which include trade-offs between measures of fairness and model performance that are not well-understood for predictive models of clinical outcomes. To inform the ongoing debate, we conduct an empirical study to characterize the impact of penalizing group fairness violations on an array of measures of model performance and group fairness. We repeat the analyses across multiple observational healthcare databases, clinical outcomes, and sensitive attributes. We find that procedures that penalize differences between the distributions of predictions across groups induce nearly-universal degradation of multiple performance metrics within groups. On examining the secondary impact of these procedures, we observe heterogeneity of the effect of these procedures on measures of fairness in calibration and ranking across experimental conditions. Beyond the reported trade-offs, we emphasize that analyses of algorithmic fairness in healthcare lack the contextual grounding and causal awareness necessary to reason about the mechanisms that lead to health disparities, as well as about the potential of algorithmic fairness methods to counteract those mechanisms. In light of these limitations, we encourage researchers building predictive models for clinical use to step outside the algorithmic fairness frame and engage critically with the broader sociotechnical context surrounding the use of machine learning in healthcare.
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 find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data 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.
Machine learning for building energy prediction has exploded in popularity in recent years, yet understanding its limitations and potential for improvement are lacking. The ASHRAE Great Energy Predictor III (GEPIII) Kaggle competition was the largest building energy meter machine learning competition ever held with 4,370 participants who submitted 39,403 predictions. The test data set included two years of hourly electricity, hot water, chilled water, and steam readings from 2,380 meters in 1,448 buildings at 16 locations. This paper analyzes the various sources and types of residual model error from an aggregation of the competitions top 50 solutions. This analysis reveals the limitations for machine learning using the standard model inputs of historical meter, weather, and basic building metadata. The types of error are classified according to the amount of time errors occur in each instance, abrupt versus gradual behavior, the magnitude of error, and whether the error existed on single buildings or several buildings at once from a single location. The results show machine learning models have errors within a range of acceptability on 79.1% of the test data. Lower magnitude model errors occur in 16.1% of the test data. These discrepancies can likely be addressed through additional training data sources or innovations in machine learning. Higher magnitude errors occur in 4.8% of the test data and are unlikely to be accurately predicted regardless of innovation. There is a diversity of error behavior depending on the energy meter type (electricity prediction models have unacceptable error in under 10% of test data, while hot water is over 60%) and building use type (public service less than 14%, while technology/science is just over 46%).
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