Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.

ABSTRACT:

Background and purpose

Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification.

Methods

We conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers. Patients who were suspected of stroke by the EMS or had hospital-diagnosed stroke were included in our cohort. Text within EMS reports were converted to unigram features, which were given as input to a support-vector machine classifier that was trained on 70% of the cohort and tested on the remaining 30%. Outco

SUBMITTER: Mayampurath A 

PROVIDER: S-EPMC8514137 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature