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To develop consensus-based core competencies for an entry-level graduate Consumer Health Informatics (CHI) course using a rigorous eDelphi methodology.

To compare differences in risk factors and 90-day mortality prediction from 2 machine learning (ML) models with a previously published non-ML model and investigate their validity in an external cohort.

To develop the authentication and authorization module for iAgree, a privacy-preserving platform that enables patients to manage consent preferences and share electronic health record (EHR) data across multiple health systems with researchers. The consent and blockchain modules are described elsewhere.

To synthesize and consolidate the existing evidence on the use and characteristics of Free/Libre and Open Source Software (FLOSS)-based electronic health records (EHRs) in global healthcare settings.

Computational phenotypes can be used to improve patient selection for observational studies. We sought to derive and validate a computational phenotype for pediatric asthma exacerbation requiring hospitalization.

Despite the increased availability of electronic health records, open-source standardized data collection to facilitate high-resolution data during extracorporeal life support (ECLS) is lacking. This project aimed to assess the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for interoperability to store data sufficiently generated in the context of ECLS and to develop a custom data model expansion in case the OMOP CDM proved insufficient.

Early prediction of in-hospital death remains a significant challenge due to the limited availability of structured data during initial admission. Unstructured clinical notes, which often contain important observations and impressions, are an underutilized resource for real-time risk stratification. While leveraging recent advances in large language models (LLMs) is a promising approach to use this unstructured information, the lack of understanding of the uncertainty of LLM predictions, at the patient level [...]

We aimed to (1) quantify changes in discrimination when adding intraoperative data to preoperative data and (2) compare tabular machine learning with feature engineering against a time-aware LSTM-based model.

Emergency department (ED) returns for mental health (MH) conditions pose a substantial burden on healthcare systems. Traditional machine learning (ML) models have shown promise in predicting ED returns but often fall short in clinical interpretability. This study evaluates whether integrating Large Language Models (LLMs) with ML methods can enhance both predictive performance and interpretability.

To facilitate responsible biomedical data sharing, large-scale data harmonization, and cutting-edge artificial intelligence (AI) applications by augmenting controlled-access data dissemination models (DDMs) with Data-derived Identity Verification (DIVe).