AIMC Topic: Electronic Health Records
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A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2025
OBJECTIVE: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the...
Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2025
BACKGROUND: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research...
A Guide for Implementing an A.I-Driven Initiative in Rural Northern Ontario.
Studies in health technology and informatics
Feb 18, 2025
Diagnosing pulmonary embolism (PE) often requires specialized expertise in interpreting x-rays and radiographic images, resources that are mostly limited in rural settings. This paper explores the development of an electronic health record (EHR) syst...
Co-Designing an Electronic Health Record Derived Digital Dashboard to Support Fair-AI Applications in Mental Health.
Studies in health technology and informatics
Feb 18, 2025
Guided by interviews with end-users and in collaboration with lived-experience advisors, the Fairness Dashboard is being co-designed to promote the standardized and responsible utilization of sociodemographic data in statistical and machine learning ...
Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.
Journal of cachexia, sarcopenia and muscle
Feb 1, 2025
BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, pro...
CARE-SD: classifier-based analysis for recognizing provider stigmatizing and doubt marker labels in electronic health records: model development and validation.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2025
OBJECTIVE: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.
Application of large language models in clinical record correction: a comprehensive study on various retraining methods.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2025
OBJECTIVES: We evaluate the effectiveness of large language models (LLMs), specifically GPT-based (GPT-3.5 and GPT-4) and Llama-2 models (13B and 7B architectures), in autonomously assessing clinical records (CRs) to enhance medical education and dia...
Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2025
OBJECTIVES: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text so...
Ambient artificial intelligence scribes: utilization and impact on documentation time.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2025
OBJECTIVES: To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.
Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2025
BACKGROUND: Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communicatio...
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