Clinical Manifestations
View abstract on PubMed
Summary
This summary is machine-generated.A new GPT-4o framework accurately stages cognitive impairment (CI) using electronic health records, outperforming existing models. This AI tool aids dementia diagnosis but requires human oversight for safe clinical use.
Area Of Science
- Artificial Intelligence
- Natural Language Processing
- Medical Informatics
Background
- Alzheimer's Disease and Related Dementias (ADRD) pose public health challenges, necessitating accurate cognitive impairment (CI) diagnosis.
- Electronic health records (EHRs) contain valuable CI data within unstructured clinical notes.
- Large language models (LLMs) like GPT-4o offer potential for analyzing EHR data for CI identification and staging.
Purpose Of The Study
- To develop and evaluate a GPT-4o-powered framework for CI staging and automated Clinical Dementia Rating (CDR) scoring using EHR data.
- To compare the performance of the GPT-4o framework against traditional NLP and embedding-based models for CI classification.
Main Methods
- A GPT-4o framework was developed for CI staging, incorporating data querying, feature extraction, and classification on 1002 Medicare patients.
- GPT-4o generated multi-note summaries and performed ordinal CI classification with confidence levels.
- Automated CDR scoring was achieved using structured answer templates, retrieval-augmented generation (RAG), and CDR domain counts with confidence levels.
Main Results
- The GPT-4o framework demonstrated high accuracy in CI staging (weighted Cohen's kappa = 0.95, Spearman correlation = 0.93).
- Performance was superior to traditional feature extraction and embedding-based models, especially for high-confidence predictions.
- Automated CDR scoring using domain counts achieved high accuracy (weighted Cohen's kappa = 0.83).
Conclusions
- The GPT-4o framework shows significant potential for automated chart review in dementia diagnosis, outperforming existing models.
- Clinical deployment necessitates a human-in-the-loop approach to mitigate misclassification risks and ensure safety.
- This hybrid AI-human model balances AI's diagnostic capabilities with interpretability and risk management.
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