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Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data.

The objective of this study is to provide an overview of the Regenstrief Teaching Electronic Medical Record (tEMR), how the tEMR could be used, and how it is currently being used in health professions education. The tEMR is a derivative of a real-world electronic health record (EHR), a large, pseudonymized patient database, and a population health tool designed to support curricular goals. The tEMR has been successfully adopted at 12 [...]

To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation.

The use of alerts from the Bronx RHIO, a health information exchange (HIE) to identify James J. Peters VAMC patients diagnosed with COVID-19 in the community was described to facilitate COVID-19 VA primary care follow-up. COVID-19 hospitalization and testing alerts were delivered on a Bronx RHIO facility report. VA COVID-19 follow-up care by telephone and video was guided by local COVID-19 clinical pathways, electronic health record (EHR) templates, and tracking [...]

To propose a visual display-the probability threshold plot (PTP)-that transparently communicates a predictive models' measures of discriminative accuracy along the range of model-based predicted probabilities (Pt).

Fertility is becoming increasingly supported by consumer health technologies, especially mobile apps that support self-tracking activities. However, it is not clear whether the apps support the variety of goals and life events of those who menstruate, especially during transitions between them.

How clinicians utilize medically actionable genomic information, displayed in the electronic health record (EHR), in medical decision-making remains unknown. Participating sites of the Electronic Medical Records and Genomics (eMERGE) Network have invested resources into EHR integration efforts to enable the display of genetic testing data across heterogeneous EHR systems. To assess clinicians' engagement with unsolicited EHR-integrated genetic test results of eMERGE participants within a large tertiary care academic medical center [...]

Seizure forecasting algorithms have become increasingly accurate and may reduce the morbidity and mortality caused by seizure unpredictability. Translating these benefits into meaningful health outcomes for people with epilepsy requires effective data visualization of algorithm outputs. To date, no studies have investigated patient and physician perspectives on effective translation of algorithm outputs into data visualizations through health information technology.

Research & Exploratory Analysis Driven Time-data Visualization (read-tv) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. read-tv provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes.

Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information.