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Machine learning (ML) algorithms are promising tools for managing anemia in hemodialysis (HD) patients. However, their efficacy in predicting erythropoiesis-stimulating agents (ESAs) doses remains uncertain. This study aimed to evaluate the effectiveness of a contemporary artificial intelligence (AI) model in prescribing ESA doses compared to physicians for HD patients.

To develop and disseminate a technical framework for administering the Research Participant Perception Survey (RPPS) and aggregating data across institutions using REDCap.

To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship.

This study aimed to develop and evaluate an artificial intelligence (AI)-driven system for forecasting Pediatric Emergency Department (PED) overcrowding and optimizing physician shift schedules using machine learning operations (MLOps).

Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete IE pipelines.

[This corrects the article DOI: 10.1093/jamiaopen/ooaf007.].

To assess the capacity of a bespoke artificial intelligence (AI) process to help medical writers efficiently generate quality plain language summary abstracts (PLSAs).

This work aims to develop a methodology for transforming Health Level 7 (HL7) Clinical Document Architecture (CDA) documents into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The described method seeks to improve the Extract, Transform, Load (ETL) design process by using HL7 CDA Template definitions and the CDA Refined Message Information Model (CDA R-MIM).

To explore patients' use of patient portals to access lab test results, their comprehension of lab test data, and factors associated with these.

We conducted a scoping review to identify barriers to telehealth use and uptake from the perspective of patient, provider, and system that were documented in the literature. In addition to identifying and categorizing the barriers, we aimed to assess how barriers differed for studies conducted during the COVID-19 pandemic, as well as how barriers differed between the United States vs internationally based studies.