SCD-Tron: Leveraging Large Clinical Language Model for Early Detection of Cognitive Decline from Electronic Health Records
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Background
Early detection of cognitive decline during the preclinical stage of Alzheimer’s disease is crucial for timely intervention and treatment. Clinical notes, often found in unstructured electronic health records (EHRs), 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.
Methods
We collected clinical notes from 2,166 patients spanning the 4 years preceding their initial mild cognitive impairment (MCI) diagnosis from the Enterprise Data Warehouse (EDW) of Mass General Brigham (MGB). To train the model, we developed SCD-Tron, a large clinical language model on 4,949 note sections labeled by experts. For evaluation, the trained model was applied to 1,996 independent note sections to assess its performance on real-world unstructured clinical data. Additionally, we used explainable AI techniques, specifically SHAP values, to interpret the models predictions and provide insight into the most influential features. Error analysis was also facilitated to further analyze the model’s prediction.
Results
SCD-Tron significantly outperforms baseline models, achieving notable improvements in precision, recall, and AUC metrics for detecting Subjective Cognitive Decline (SCD). Tested on many real-world clinical notes, SCD-Tron demonstrated high sensitivity with only one false negative, crucial for clinical applications prioritizing early and accurate SCD detection. SHAP-based interpretability analysis highlighted key textual features contributing to model predictions, supporting transparency and clinician understanding.
Conclusion
SCD-Tron offers a novel approach to early cognitive decline detection by applying large clinical language models to unstructured EHR data. Pretrained on real-world clinical notes, it accurately identifies early cognitive decline and integrates SHAP for interpretability, enhancing transparency in predictions.
