AIMC Topic: Electronic Health Records
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Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms.
Scientific reports
Apr 5, 2025
Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter sur...
Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.
International journal of medical informatics
Apr 4, 2025
OBJECTIVE: Determine the efficacy of commonly used approaches to handling missing and/or imbalanced Electronic Health Record (EHR) data on the performance of predictive models targeting risk of admission, intensive care unit (ICU) use, or prolonged l...
MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs.
Cell reports. Medicine
Apr 4, 2025
Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis and emergency condition identification. To address these limitations, we develop Meta General Practiti...
Estimating depression severity in narrative clinical notes using large language models.
Journal of affective disorders
Apr 3, 2025
BACKGROUND: Depression treatment guidelines emphasize measurement-based care using patient-reported outcome measures, yet their impact on narrative documentation quality remains underexplored.
Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.
Nature medicine
Apr 3, 2025
Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and repeated hospital admissions. Routine screening for patients at risk for an OUD to prevent complications is not standard practice in many hospitals, lead...
Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis.
JMIR medical informatics
Apr 2, 2025
BACKGROUND: The use of patient-reported outcome measures (PROMs) is an expected component of high-quality, measurement-based chiropractic care. The largest health care system offering integrated chiropractic care is the Veterans Health Administration...
InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Apr 2, 2025
Estimation of cardiovascular model parameters from electronic health records (EHRs) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to ...
Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.
Journal of biomedical informatics
Apr 1, 2025
OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subpheno...
Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.
BMC medical informatics and decision making
Mar 31, 2025
BACKGROUND: The integration of big data and artificial intelligence (AI) in healthcare, particularly through the analysis of electronic health records (EHR), presents significant opportunities for improving diagnostic accuracy and patient outcomes. H...
Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study.
JMIR aging
Mar 31, 2025
BACKGROUND: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes.
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