AIMC Topic: Electronic Health Records

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Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.

JCO clinical cancer informatics
PURPOSE: Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with natural language processing (NLP). However, more efficient NLP systems are needed to detect various symptoms and distinguish observed symptoms from neg...

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence...

Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access app...

A general framework for developing computable clinical phenotype algorithms.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machi...

Development of a Mapping Table for Nursing Notes Based on Nurses' Concerns in ICU Patients.

Studies in health technology and informatics
This study aimed to develop a mapping table that connects nursing notes with standard terminology, focusing on nurses' concerns for ICU patients. After extracting nursing notes from a publicly accessible database, a research team, including a nursing...

Development of a Nursing Diagnosis/Record Generative AI System Based on Virtual Patient Data.

Studies in health technology and informatics
This study investigates how to reduce nurses' repetitive electronic nursing record tasks. We applied generative AI by learning nursing record data practiced with virtual patient data. We aim to evaluate generative AI's usefulness, usability, and avai...

Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records.

Studies in health technology and informatics
This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing on nurses' concerns reflected in nursing records from the MIMIC IV database. We included 46,693 first-time ICU admissions of adults over 18 years with ...

Deep Learning for Predicting Phlebitis in Patients with Intravenous Catheters.

Studies in health technology and informatics
This study presents a deep learning model to predict phlebitis in patients with peripheral intravenous catheter (PIVC) insertions. Leveraging electronic health record data from 27,532 admissions and 70,293 PIVC events at a hospital in Seoul, South Ko...

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Studies in health technology and informatics
Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In thi...

Fairness in Classifying and Grouping Health Equity Information.

Studies in health technology and informatics
This paper explores the balance between fairness and performance in machine learning classification, predicting the likelihood of a patient receiving anti-microbial treatment using structured data in community nursing wound care electronic health rec...