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StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.
- Source :
- NPJ Digital Medicine; 5/17/2024, Vol. 7 Issue 1, p1-22, 22p
- Publication Year :
- 2024
-
Abstract
- Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system. [ABSTRACT FROM AUTHOR]
- Subjects :
- CONSENSUS (Social sciences)
NEUROLOGISTS
RESEARCH funding
CLINICAL decision support systems
ARTIFICIAL intelligence
NATURAL language processing
AGE distribution
CLASSIFICATION
CEREBRAL arteries
ISCHEMIC stroke
ELECTRONIC health records
ATRIAL fibrillation
MACHINE learning
ARTERIAL occlusions
AUTOMATION
Subjects
Details
- Language :
- English
- ISSN :
- 23986352
- Volume :
- 7
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Digital Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 177311006
- Full Text :
- https://doi.org/10.1038/s41746-024-01120-w