AIMC Topic: Electronic Health Records

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Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
Excessive documentation burden is linked to clinician burnout, thus motivating efforts to reduce burden. Generative artificial intelligence (AI) poses opportunities for burden reduction but requires rigorous assessment. We evaluated the ability of a ...
Documentation Electronic Health Records Nursing Records Natural Language Processing Humans Artificial Intelligence
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BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancement, leveraging the depth of clinical notes for improved decision-making. Despite their success, the...
Computer Security Electronic Health Records Decision Support Systems, Clinical Hospital Mortality Humans Natural Language Processing
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Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
Electronic Health Record (EHR) audit log data are increasingly utilized for clinical tasks, from workflow modeling to predictive analyses of discharge events, adverse kidney outcomes, and hospital readmissions. These data encapsulate user-EHR interac...
Patient Discharge Electronic Health Records Medical Audit Natural Language Processing Humans Large Language Models
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Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models c...
Primary Health Care Canada Diabetes Mellitus Logistic Models Electronic Health Records Adult Humans Machine Learning
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Combining Rule-based NLP-lite with Rapid Iterative Chart Adjudication for Creation of a Large, Accurately Curated Cohort from EHR data: A Case Study in the Context of a Clinical Trial Emulation.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
The aim of this work was to create a gold-standard curated cohort of 10,000+ cases from the Veteran Affairs (VA) corporate data warehouse (CDW) for virtual emulation of a randomized clinical trial (CSP#592). The trial had six inclusion/exclusion crit...
Natural Language Processing Humans Information Storage and Retrieval Cohort Studies Algorithms United States Electronic Health Records Randomized Controlled Trials as Topic
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Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to forecast the potential diagnosi...
Diagnosis International Classification of Diseases Disease Natural Language Processing Semantics Machine Learning Electronic Health Records Humans
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Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined mach...
Narration Natural Language Processing Risk Assessment Neoplasms Heart Failure Large Language Models Humans Electronic Health Records Female Machine Learning
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A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances...
Humans Artificial Intelligence Machine Learning Natural Language Processing Large Language Models Language Phenotype Deep Learning Electronic Health Records
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Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
Efficient querying for medication information in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. To address the complexity and data volume challenges involved in efficient medication information re...
COVID-19 Semantics Electronic Health Records Biological Ontologies Humans SARS-CoV-2 Information Storage and Retrieval
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Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged as a promising approach for improving healthcare delivery and patient outcomes. In this work, we explore the integration of RPM features - including a...
Humans Monitoring, Physiologic Emergency Service, Hospital Remote Patient Monitoring Machine Learning Electronic Health Records
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