TAMJISTROKE is a transparent hybrid expert system that localizes stroke lesions from real-world clinical notes using LLM-assisted text processing and rule-based reasoning. We conducted an observational study using deidentified MIMIC-IV discharge summaries, applying TAMJISTROKE, a three-layer modular pipeline that integrates large language model-based structured symptom extraction from chief complaints and neurologic examinations with rule-score-based regional and vascular territory localization. A total of 367 filtered ischemic stroke cases were evaluated across three trials, with performance measured with accuracy, sensitivity, specificity, F1 score, and structured error analyses.
TAMJISTROKE showed high functional output. Brain region localization was more challenging (accuracy 0.71, sensitivity 0.73, specificity 0.70, F1 0.51), performing better on hemispheric than posterior fossa lesions. Vascular territory prediction was strong (accuracy 0.97, sensitivity 0.93, specificity 0.98, micro-averaged F1 0.93, macro-averaged F1 0.61, versus naïve baseline micro/macro F1 of 0.87 and 0.19), though posterior fossa strokes again lagged.
Case-based analysis aligned with the trial-based results. Error analysis identified ambiguous or broadly localizable clinical documentation as the main source of errors; excluding these cases in post-hoc analysis improved performance across all vascular territories. By combining LLM-based symptom structuring with rule-based localization, this multimodular system achieved reproducible stroke localization from real-world free-text clinical notes, highlighting its potential utility for education, retrospective clinical review, and research.
Department of Neurology, State University of New York Downstate Health Sciences University, Brooklyn, NY, 11203, USA Jung-Hyun Lee, Sujith Vasireddy, Shih Syuan Wang, Svetlana Kozlova, Sergio L. Lytton Department of Neurology, Kings County Hospital, Brooklyn, NY, 11203, USA Department of Neurology, Maimonides Medical Center, Brooklyn, NY, 11219, USA Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, 06520, USA Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06510, USA Wu-Tsai Institute, Yale University, New Haven, CT, 06510, USA Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, 06510, USA Neurosimulation Laboratory, State University of New York Downstate Health Sciences University, Brooklyn, NY, 11203, USA Jaffe Comprehensive Stroke Center, Maimonides Medical Center, Brooklyn, NY, 11219, USA Department of Physiology & Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, 11203, USA The authors declare no competing interests. The use of MIMIC-IV data has been approved by the Institutional Review Board of Beth Israel Deaconess Medical Center, with a waiver of informed consent because the database consists entirely of deidentified data.
This study was conducted in accordance with institutional guidelines and the Declaration of Helsinki. The researchers with data access (JL, SV, SSW) obtained necessary permissions to access the MIMIC-IV database.18. Jung-Hyun Lee, MD, Sujith Vasireddy, MD, and Shih Syuan Wang, MD, had full access to all the data in the study and took responsibility for the integrity of the data and accuracy of the analysis.
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