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Building on innovations for autism detection-where artificial intelligence (AI)-based models monitor clinical data within electronic health records-this study evaluates the context for clinical decision support (CDS) deployment and identifies design preferences.

Clinical deterioration in hospitalized patients is often preventable, but traditional early warning scores based on structured data are limited by delayed or inconsistent documentation. We developed and evaluated a machine learning pipeline that predicts clinical deterioration using real-time pager messages exchanged between clinicians.

Participation in cancer clinical trials is low in community oncology settings, partly because institution-specific trial information is fragmented. We evaluated feasibility of embedding curated trial content in an AI-enabled knowledge management application.

To assess whether existing nationwide Health Information Networks (HINs) and Health Information Exchanges (HIEs) can be leveraged for electronic health record (EHR) data acquisition for research-specifically, for the NIH's All of Us Research Program.

To develop and evaluate an automated algorithm to identify sepsis onset, referred to as time-zero (t0), from the electronic medical records (EMR).

Accurate and timely prediction of physiological deterioration in hospitalized patients is essential, but existing deep learning models, which rely solely on structured electronic health record (EHR) data, generate high false-positive rates by missing crucial contextual information in clinical notes. We present DETERIO-LLM, a hybrid prediction model that selectively integrates large language models (LLMs) with a deep learning model (DETERIO) to reclassify borderline-risk alerts using narrative clinical notes.

Social determinants of health (SDOH) are incompletely captured in structured electronic health records (EHRs) but are frequently documented in unstructured clinical notes. We evaluated large language models (LLMs) for extracting SDOH from clinical text.

To develop a generalizable framework for identifying algorithmic discrimination risks arising from subgroup imbalances in machine learning training data, with relevance to medical informatics applications where heterogeneous real-world data can bias model behavior.

Pharmacogenomics can optimize therapeutic outcomes while minimizing adverse events. However, the lack of trained healthcare professionals has impeded its widespread adoption. Current patient-facing applications are either institution-specific or vary significantly in their coverage of genetic variants, interpretation guidelines, and data privacy protection. To address these shortcomings, we developed PharMe.