Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Abstract

ObjectiveTo conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open challenges in building deep learning models of EHRs.Design/methodWe searched PubMed and Google Scholar for papers on deep learning studies using EHR data published between January 1, 2010, and January 31, 2018. We summarize them according to these axes: types of analytics tasks, types of deep learning model architectures, special challenges arising from health data and tasks and their potential solutions, as well as evaluation strategies.ResultsWe surveyed and analyzed multiple aspects of the 98 articles we found and identified the following analytics tasks: disease detection/classification, sequential prediction of clinical events, concept embedding, data augmentation, and EHR data privacy. We then studied how deep architectures were applied to these tasks. We also discussed some special challenges arising from modeling EHR data and reviewed a few popular approaches. Finally, we summarized how performance evaluations were conducted for each task.DiscussionDespite the early success in using deep learning for health analytics applications, there still exist a number of issues to be addressed. We discuss them in detail including data and label availability, the interpretability and transparency of the model, and ease of deployment.

A Path for Translation of Machine Learning Products into Healthcare Delivery

“…9 Automation tasks are cases in which "a machine operates independently to complete a task," whereas clinical decision support tasks are cases in which "a machine is concerned with providing information or assistance to the primary agent responsible for task completion." 9 Distinct from prior systematic reviews of EHR models, 10,11 the current review focusses on models that have been productised and integrated into clinical care rather than the large body of academic work of published models that are not integrated. The review builds upon related work that highlights how academic and industry partners collaborate to develop machine learning products, 12 as well as the need for engagement from front-line clinicians and standard reporting.…”

Development of an interactive open source software application (RadaR) for infection management / antimicrobial stewardship

“…Next steps will involve testing in multiple settings and forming a user and research group to continue and expand the use of open source technology and open science principles in infection management. This can lead to more transparency enhancing optimal quality of care and patient safety which is crucial in the light of new data-driven developments of using EHR (24) .…”

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

“…Longitudinal-based methods leverage the temporal depen-dencies among clinical events, see (Choi et al 2016b;Choi et al 2016a;Lipton et al 2015;Le, Tran, and Venkatesh 2018;Xiao, Choi, and Sun 2018). Among them, RETAIN (Choi et al 2016b) is based on a two-level neural attention model which detects influential past visits and significant clinical variables within those visits.…”