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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.

The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.

Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for [...]

While federal regulation provides patients the right to access their electronic health records and promotes increased use of health information technology, patient access to electronic health records remains limited. The 21st Century Cures Act, signed into law over a year ago, has important provisions that could significantly improve access and availability of health data. Specifically, the provisions call for partnerships among health information exchange networks, educational and research initiatives, and [...]

Online peer support groups are an increasingly common venue for caregivers supporting disabled family members to exchange informational, emotional, and instrumental support. We know very little, however, about who uses these groups and whether they are reaching those with the greatest needs.

Hospitals that routinely share patients are those that most critically need to engage in electronic health information exchange (HIE) with each other to ensure clinical information is available to inform treatment decisions. We surveyed pairs of hospitals in a nationwide sample to describe whether and how hospitals within each hospital referral region (HRR) that have the highest shared patient (HSP) volume engaged in HIE with each other.

To test the hypothesis that use of a clinical decision support (CDS) system in a primary care setting can reduce cardiovascular (CV) risk in patients.

Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes.