An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.

Background: Diagnosis of venous thromboembolism (VTE) is often delayed, and facilitating earlier diagnosis may improve associated morbidity and mortality. Clinical notes contain information not found elsewhere in the medical record that could facilitate timely VTE diagnosis and accurate quality measurement. However, extracting relevant information from unstructured clinical notes is complex.

CD-Tron: Leveraging large clinical language model for early detection of cognitive decline from electronic health records.

Background: Early detection of cognitive decline during the preclinical stage of Alzheimer's disease and related dementias (AD/ADRD) is crucial for timely intervention and treatment. Clinical notes in the electronic health record contain valuable information that can aid in the early identification of cognitive decline. In this study, we utilize advanced large clinical language models, fine-tuned on clinical notes, to improve the early detection of cognitive decline.

Extracting Cognitive Impairment Assessment Information From Unstructured Notes in Electronic Health Records Using Natural Language Processing Tools: Validation with Clinical Assessment Data.

Purpose: We aimed to develop a Natural Language Processing (NLP) algorithm to extract cognitive scores from electronic health records (EHR) data and compare them with cognitive function recorded by Centers for Medicare & Medicaid Services (CMS)-mandated clinical assessments in nursing homes and home health visits.

Patients And Methods: We identified a cohort of Medicare beneficiaries who had either the Minimum Data Set (MDS) or Outcome and Assessment Information Set (OASIS) linked to EHR data from the Research Patient Data Registry (Mass General Brigham, Boston, MA) from 2010 to 2019. We applied an NLP approach to identify the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) scores from unstructured clinician notes in EHR.

Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.

Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.

CD-Tron: Leveraging Large Clinical Language Model for Early Detection of Cognitive Decline from Electronic Health Records.

Background: Early detection of cognitive decline during the preclinical stage of Alzheimer's disease and related dementias (AD/ADRD) is crucial for timely intervention and treatment. Clinical notes in the electronic health record contain valuable information that can aid in the early identification of cognitive decline. In this study, we utilize advanced large clinical language models, fine-tuned on clinical notes, to improve the early detection of cognitive decline.

Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records.

Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.

Enhancing Early Detection of Cognitive Decline in the Elderly: A Comparative Study Utilizing Large Language Models in Clinical Notes.

Background: Large language models (LLMs) have shown promising performance in various healthcare domains, but their effectiveness in identifying specific clinical conditions in real medical records is less explored. This study evaluates LLMs for detecting signs of cognitive decline in real electronic health record (EHR) clinical notes, comparing their error profiles with traditional models. The insights gained will inform strategies for performance enhancement.