Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

ABSTRACT: Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassify ischemic stroke events and do not distinguish acuity or location. Expeditious, accurate data extraction could provide considerable improvement in identifying stroke in large datasets, triaging critical clinical reports, and quality improvement efforts. In this study, we developed and report a comprehensive framework studying the performance of simple and complex stroke-specific Natural Language Processing (NLP) and Machine Learning (ML) methods to determine presence, location, and acuity of ischemic stroke from radiographic text. We collected 60,564 Computed Tomography and Magnetic Resonance Imaging Radiology reports from 17,864 patients fr

SUBMITTER: Ong CJ 

PROVIDER: S-EPMC7304623 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature