Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument Validation Study
| Autor: | Amy Y.X. Yu, Chloe Pou-Prom, Zhongyu A Liu, Kaitlyn Lopes, Richard I. Aviv, Moira K. Kapral, Muhammad Mamdani |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
neurovascular diagnostic imaging Computer applications to medicine. Medical informatics R858-859.7 Health Informatics Perfusion scanning data extraction computer.software_genre 03 medical and health sciences 0302 clinical medicine Health Information Management Chart Occlusion Medical imaging medicine stroke surveillance 030212 general & internal medicine natural language processing Stroke Original Paper medicine.diagnostic_test business.industry imaging Neurovascular bundle medicine.disease stroke Data extraction Angiography surveillance Radiology Artificial intelligence business computer 030217 neurology & neurosurgery Natural language processing |
| Zdroj: | JMIR Medical Informatics JMIR Medical Informatics, Vol 9, Iss 5, p e24381 (2021) |
| ISSN: | 2291-9694 |
| Popis: | Background Diagnostic neurovascular imaging data are important in stroke research, but obtaining these data typically requires laborious manual chart reviews. Objective We aimed to determine the accuracy of a natural language processing (NLP) approach to extract information on the presence and location of vascular occlusions as well as other stroke-related attributes based on free-text reports. Methods From the full reports of 1320 consecutive computed tomography (CT), CT angiography, and CT perfusion scans of the head and neck performed at a tertiary stroke center between October 2017 and January 2019, we manually extracted data on the presence of proximal large vessel occlusion (primary outcome), as well as distal vessel occlusion, ischemia, hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status (secondary outcomes). Reports were randomly split into training (n=921) and validation (n=399) sets, and attributes were extracted using rule-based NLP. We reported the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the overall accuracy of the NLP approach relative to the manually extracted data. Results The overall prevalence of large vessel occlusion was 12.2%. In the training sample, the NLP approach identified this attribute with an overall accuracy of 97.3% (95.5% sensitivity, 98.1% specificity, 84.1% PPV, and 99.4% NPV). In the validation set, the overall accuracy was 95.2% (90.0% sensitivity, 97.4% specificity, 76.3% PPV, and 98.5% NPV). The accuracy of identifying distal or basilar occlusion as well as hemorrhage was also high, but there were limitations in identifying cerebral ischemia, ASPECTS, and collateral status. Conclusions NLP may improve the efficiency of large-scale imaging data collection for stroke surveillance and research. |
| Databáze: | OpenAIRE |
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