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We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distance traveled.

To facilitate the development of standards-based clinical decision support (CDS) systems, we review the current set of CDS standards that are based on Health Level Seven International Fast Healthcare Interoperability Resources (FHIR). Widespread adoption of these standards may help reduce healthcare variability, improve healthcare quality, and improve patient safety.

To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician-computer models.

This study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.

To assess primary care teams' perceptions of a health information exchange (HIE) event notification intervention for geriatric patients in 2 Veterans Health Administration (VHA) medical centers.

The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production [...]

To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.

The study sought to assess the clinical performance of a machine learning model aiming to identify unusual medication orders.

The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep [...]

We quantify the use of clinical decision support (CDS) and the specific barriers reported by ambulatory clinics and examine whether CDS utilization and barriers differed based on clinics' affiliation with health systems, providing a benchmark for future empirical research and policies related to this topic.