AIMC Topic: Electronic Health Records
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Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study.
JMIR aging
Feb 25, 2025
BACKGROUND: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Knowledge about the path from initial memory loss complaints to dementia diagnosis remains limited.
CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning.
Journal of biomedical informatics
Feb 24, 2025
Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundation for constructing medical knowledge graphs and supporting downstream tasks. Chinese EMRs face challenges in accurately extracting medical entity rela...
Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.
Scientific reports
Feb 24, 2025
In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyzing the preventive effects of cardioprotective drugs against anthracycline-induced cardiotoxicity (A...
Humans
Retrospective Studies
Electronic Health Records
Female
Aged
Adult
Cardiotonic Agents
Cardiotoxicity
Anthracyclines
Adrenergic beta-Antagonists
Calcium Channel Blockers
Male
Hydroxymethylglutaryl-CoA Reductase Inhibitors
Middle Aged
Angiotensin Receptor Antagonists
Drug Repositioning
Angiotensin-Converting Enzyme Inhibitors
Natural Language Processing
Cognitive performance classification of older patients using machine learning and electronic medical records.
Scientific reports
Feb 24, 2025
Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improvin...
Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges.
Computer methods and programs in biomedicine
Feb 23, 2025
PURPOSE: This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this phy...
Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.
Medical image analysis
Feb 21, 2025
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a com...
Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques.
Computers in biology and medicine
Feb 20, 2025
OBJECTIVE: Given the significantly increased number of individuals prescribed stimulants in the past decade, there has been growing concern regarding the risk of cardiovascular events among adults on stimulant therapy. We aimed to quantify the added ...
Automated identification of incidental hepatic steatosis on Emergency Department imaging using large language models.
Hepatology communications
Feb 19, 2025
BACKGROUND: Hepatic steatosis is a precursor to more severe liver disease, increasing morbidity and mortality risks. In the Emergency Department, routine abdominal imaging often reveals incidental hepatic steatosis that goes undiagnosed due to the ac...
Prediction of early-onset bipolar using electronic health records.
Journal of child psychology and psychiatry, and allied disciplines
Feb 18, 2025
BACKGROUND: Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical n...
Comparative ranking of marginal confounding impact of natural language processing-derived versus structured features in pharmacoepidemiology.
Computers in biology and medicine
Feb 17, 2025
OBJECTIVE: To explore the ability of natural language processing (NLP) methods to identify confounder information beyond what can be identified using claims codes alone for pharmacoepidemiology.
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