AIMC Topic: Electronic Health Records
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Natural language processing for identifying major bleeding risk in hospitalised medical patients.
Computers in biology and medicine
Mar 30, 2025
BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence and risk factors for major bleeding in hospitalised m...
Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.
JMIR cancer
Mar 28, 2025
BACKGROUND: Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can ...
A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.
Journal of substance use and addiction treatment
Mar 22, 2025
BACKGROUND: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid ...
Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Mar 22, 2025
Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of various data types, including medical imaging, electronic health records, genomic information and real-ti...
Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.
BMJ open quality
Mar 22, 2025
OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to estimate SUD prevalence from electronic health record (EHR) data and to assess variation in facility-lev...
Natural Language Processing to Extract Head and Neck Cancer Data From Unstructured Electronic Health Records.
Clinical oncology (Royal College of Radiologists (Great Britain))
Mar 20, 2025
AIMS: Patient data is frequently stored as unstructured data within Electronic Health Records (EHRs), requiring manual curation. AI tools using Natural Language Processing (NLP) may rapidly curate accurate real-world unstructured EHRs to enrich datas...
Large language models for automating clinical trial matching.
Current opinion in urology
Mar 20, 2025
PURPOSE OF REVIEW: The uses of generative artificial intelligence (GAI) technologies in medicine are expanding, with the use of large language models (LLMs) for matching patients to clinical trials of particular interest. This review provides an over...
The advance of artificial intelligence in outpatient urology: current applications and future directions.
Current opinion in urology
Mar 20, 2025
PURPOSE OF REVIEW: Prudent integration of artificial intelligence (AI) into outpatient urology has already begun to revolutionize clinical workflows, improve administrative efficiency, and automate mundane and laborious tasks in the clinic setting.
Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.
Journal of psychopathology and clinical science
Mar 20, 2025
Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use an...
Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals.
EBioMedicine
Mar 19, 2025
BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patterns. Traditional classification systems based on left ventricular ejection fraction (LVEF) have limitations in capturing the heterogeneity of HF. We a...
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