AIMC Topic: Electronic Health Records
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Zero-shot learning for clinical phenotyping: Comparing LLMs and rule-based methods.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND: Phenotyping, the process of systematically identifying and classifying conditions within clinical data, is a crucial first step in any data science work involving Electronic Health Records (EHRs). Traditional approaches require extensive ...
The application of natural language processing technology in hospital network information management systems: Potential for improving diagnostic accuracy and efficiency.
SLAS technology
Jun 1, 2025
BACKGROUND: Processing scanned documents in electronic health records (EHR) was one of the problem in hospital network information management systems (HNIMS). To overcome this difficulty, the complex interactions among natural language processing (NL...
Recovering missing electronic health record mortality data with a machine learning-enhanced data linkage process.
Journal of the American Medical Informatics Association : JAMIA
Jun 1, 2025
OBJECTIVE: To develop a continual process for linking more comprehensive external mortality data to electronic health records (EHRs) for a large healthcare system, which can serve as a template for other healthcare systems.
Automating the Addiction Behaviors Checklist for Problematic Opioid Use Identification.
JAMA psychiatry
Jun 1, 2025
IMPORTANCE: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Electronic health records (EHR) allow large-scale studies to identify a continuum of problematic opioid use, including opioid us...
Clinician Suicide Risk Assessment for Prediction of Suicide Attempt in a Large Health Care System.
JAMA psychiatry
Jun 1, 2025
IMPORTANCE: Clinical practice guidelines recommend suicide risk screening and assessment across behavioral health settings. The predictive accuracy of real-world clinician assessments for stratifying patients by risk of future suicidal behavior, howe...
Integrating large language models with human expertise for disease detection in electronic health records.
Computers in biology and medicine
Jun 1, 2025
OBJECTIVE: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelli...
Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.
Critical care medicine
Jun 1, 2025
OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning model that retrospectively identifies patients with acute respiratory distress syndrome (acute respiratory distress syndrome [ARDS]) using electronic health re...
Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.
Journal of the American Medical Informatics Association : JAMIA
Jun 1, 2025
OBJECTIVE: Diagnosis codes documented in electronic health records (EHR) are often relied upon to clinically phenotype patients for biomedical research. However, these diagnoses can be incomplete and inaccurate, leading to false negatives when search...
Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.
Journal of the American Medical Informatics Association : JAMIA
Jun 1, 2025
OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study leveraged the Health Equity Across the AI Lifecycle (HEAAL) framework to (a) fine tune the previously buil...
Semi-Supervised PARAFAC2 Decomposition for Computational Phenotyping Using Electronic Health Records.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Computational phenotyping uses data mining methods to extract clusters of clinical descriptors, known as phenotypes, from electronic health records (EHR). Tensor factorization methods are very effective in extracting meaningful patterns and have beco...
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