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Clarify disciplinary foundations and internal structure of biomedical informatics.

To develop the first public-facing dashboard that translates genomic sequencing data from wastewater into accessible and actionable community information concerning human pathogenic viruses, representing a shift to sequencing-based public health wastewater monitoring.

Evaluate how RAG architecture, including corpus structure, retrieval strategy, and pipeline complexity, affects LLM-based medical problem solving and knowledge retrieval in sleep medicine.

Background Sepsis remains a leading cause of death for burn patients, yet the condition is hard to spot early. Hospitals generally rely on International Classification of Diseases (ICD) codes for surveillance, but these codes are assigned late and often miss active cases. Objectives We developed and validated a scalable, electronic health record (EHR)-based digital phenotype that improves identification of burn-related sepsis compared with ICD codes alone. Methods We performed a [...]

This study positions surgeon gap time, defined as the interval between consecutive surgeries performed by the same surgeon, as a surgeon-level metric of efficiency. Understanding gap time requires accounting for a surgeon's operative workload, yet no objective electronic health record (EHR)-derived measure exists. We conceptualize surgical case demand as an EHR-derived surrogate for operative workload and examine its association with surgeon gap time.

Electronic health record (EHR) data are prone to missingness and errors. Previously, we devised an enriched chart review protocol where a "roadmap" of auxiliary diagnoses was used to recover missing values. Still, chart reviews are expensive and time-intensive, limiting the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a roadmap-driven algorithm, based on International Classification of Diseases, 10th revision (ICD-10) codes, to [...]

Patient adherence to medication may be influenced by preferences regarding drug-product attributes. Product quality complaints (PQC) and medical information (MI) requests are a potential source of evidence on patients' preferences. The objective of this study was to demonstrate an approach for extracting and categorizing PQC and MI data using natural language processing (NLP) and quantify the relative desirability and acceptability of attributes of oral solid dosage formulations using sentiment analysis.

To provide a practical and methodologically grounded overview of explainable machine learning (XML) approaches in healthcare, with emphasis on their interpretation and application in clinical research and decision support. By moving beyond traditional predictive models, this primer aims to foster trust, transparency, and informed clinical decision-making, ultimately bridging the gap between data science and medical practice.

To develop a freely available, researcher-oriented system for accurate normalization of free-text medication strings to standardized RxNorm ingredient-level concepts.