AIMC Topic: Natural Language Processing

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A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Mapping electronic health records (EHR) data to common data models (CDMs) enables the standardization of clinical records, enhancing interoperability and enabling large-scale, multi-centered clinical investigations. Using 2 large publicly available d...

Towards Optimizing LLM Use in Healthcare: Identifying Patient Questions in MyChart Messages.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The volume of patient-provider messages is on the rise, and Large Language Models (LLMs) can potentially streamline the clinical messaging process, but their success hinges on triaging messages they can optimally address. In this study, we analyzed E...

Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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 ...

BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

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
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...

RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical question answering systems have the potential to provide clinicians with relevant and timely answers to their questions. Nonetheless, despite the advances that have been made, adoption of these systems in clinical settings has been slow. One...

Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

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
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...