AIMC Topic: Electronic Health Records
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Clustering Event Trajectories with Machine Learning: An Approach for Electronic Healthcare Records.
Studies in health technology and informatics
May 15, 2025
Multimorbidity is increasingly prevalent as the population ages and individuals with multiple long-term conditions (MLTCs) live longer. Often each condition is treated by a separate clinician, which can lead to harmful drug-drug and drug-disease inte...
Extracting Multifaceted Characteristics of Patients With Chronic Disease Comorbidity: Framework Development Using Large Language Models.
JMIR medical informatics
May 15, 2025
BACKGROUND: Research on chronic multimorbidity has increasingly become a focal point with the aging of the population. Many studies in this area require detailed patient characteristic information. However, the current methods for extracting such inf...
A Deep Learning-Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records.
JMIR cancer
May 15, 2025
BACKGROUND: Progression-free survival (PFS) is a crucial endpoint in cancer drug research. Clinician-confirmed cancer progression, namely real-world PFS (rwPFS) in unstructured text (ie, clinical notes), serves as a reasonable surrogate for real-worl...
Scientific Evidence for Clinical Text Summarization Using Large Language Models: Scoping Review.
Journal of medical Internet research
May 15, 2025
BACKGROUND: Information overload in electronic health records requires effective solutions to alleviate clinicians' administrative tasks. Automatically summarizing clinical text has gained significant attention with the rise of large language models....
Breaking Digital Health Barriers Through a Large Language Model-Based Tool for Automated Observational Medical Outcomes Partnership Mapping: Development and Validation Study.
Journal of medical Internet research
May 15, 2025
BACKGROUND: The integration of diverse clinical data sources requires standardization through models such as Observational Medical Outcomes Partnership (OMOP). However, mapping data elements to OMOP concepts demands significant technical expertise an...
Identifying Symptom Information in Clinical Notes Using Natural Language Processing.
Nursing research
May 13, 2025
BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used ...
Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes.
Journal of medical Internet research
May 12, 2025
Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and ...
Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.
Nature communications
May 12, 2025
Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a significant challenge in practice. Unsupervised machine learning methods have been proposed to address this ...
Evaluation and Bias Analysis of Large Language Models in Generating Synthetic Electronic Health Records: Comparative Study.
Journal of medical Internet research
May 12, 2025
BACKGROUND: Synthetic electronic health records (EHRs) generated by large language models (LLMs) offer potential for clinical education and model training while addressing privacy concerns. However, performance variations and demographic biases in th...
Cross language transformation of free text into structured lobectomy surgical records from a multi center study.
Scientific reports
May 2, 2025
In a recent study, the effectiveness of GPT-4 Omni in transforming lobectomy surgical records into structured data across multiple languages was explored. The aim was to improve both efficiency and accuracy in documenting thoracic surgical oncology p...
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