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

Emily Kogan, Kathryn Twyman, Jesse Heap, Dejan Milentijevic, Jennifer H. Lin, Mark Alberts

Research output: Contribution to journalArticle

2 Scopus citations

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

Original languageEnglish (US)
Article number8
JournalBMC Medical Informatics and Decision Making
Volume20
Issue number1
DOIs
StatePublished - Jan 8 2020

Keywords

  • Database
  • Outcomes research
  • Real-world evidence

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

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