Assessing stroke severity using electronic health record data: a machine learning approach.

ABSTRACT:

Background

Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structured real-world evidence databases seldom include the severity. The aim of this study was to use machine learning models to impute NIHSS scores for all patients with newly diagnosed stroke from multi-institution electronic health record (EHR) data.

Methods

NIHSS scores available in the Optum© de-identified Integrated Claims-Clinical dataset were extracted from physician notes by applying natural language processing (NLP) methods. The cohort analyzed in the study consists of the 7149 patients with an inpatient or emergency room diagn

SUBMITTER: Kogan E 

PROVIDER: S-EPMC6950922 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature