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

By Cao Xiao, Edward Choi, Jimeng Sun. 2018

716 citations recorded by the source corpus.

Abstract Objective To 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/method We 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. Results We 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. Discussion Despite 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.

Subfields: Artificial Intelligence, Health Information Management

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “Not only medical technology companies, but also for example Google Brain [28–30],4 DeepMind [31],5 Microsoft [32,33]6 and IBM [34].”
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020, background): “RNNs, and in particular LSTMs, have achieved state of the art performance on a wide variety of time series regression tasks, including healthcare related tasks [17–19].”
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination (2018, methodology): “Longitudinal-based methods leverage the temporal dependencies among clinical events, see [3, 4, 5, 6, 19].”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “vided state-of-the-art performance for various healthcare tasks including medical image reconstruction [1], management of electronic health records [2], cancer segmentation [3], disease prediction [4], clinical imaging [5], image retrieval [6], and computational biology [7].”
Pre-training of Graph Augmented Transformers for Medication Recommendation (2019, methodology): “A number of deep learning models were proposed to assist doctors in making medication recommendation [Xiao et al. , 2018a; Shang et al. , 2019; Baytas et al. , 2017; Choi et al. , 2018; Ma et al. , 2018].”
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (2019, methodology): “Machine learning techniques leverage structured features in ehr data, such as lab results and electrocardiography measurements, to uncover patterns and improve predictions (Shickel et al., 2018; Xiao et al., 2018a; Yu et al., 2018).”