AIMC Topic: Electronic Health Records

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Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records.

Birth defects research
BACKGROUND: International Classification of Disease (ICD) codes can accurately identify patients with certain congenital heart defects (CHDs). In ICD-defined CHD data sets, the code for secundum atrial septal defect (ASD) is the most common, but it h...

Development of secure infrastructure for advancing generative artificial intelligence research in healthcare at an academic medical center.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Generative AI, particularly large language models (LLMs), holds great potential for improving patient care and operational efficiency in healthcare. However, the use of LLMs is complicated by regulatory concerns around data security and p...

RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplements.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records.

A dataset and benchmark for hospital course summarization with adapted large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare appl...

A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.

Journal of the American Medical Informatics Association : JAMIA
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
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
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
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
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
OBJECTIVE: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.