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Population-Level Digital Stroke Surveillance: Building a Fair and Accurate ICD-10 Detection Model.

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This study developed a machine learning algorithm using International Classification of Diseases, 10th Revision (ICD-10) codes to accurately detect acute ischemic stroke (AIS) across diverse populations, ensuring equitable health surveillance.

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Acute ischemic strokeInternational Classification of Disease-10Machine learning algorithmsRacial equity in healthcareStroke surveillance

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Area of Science:

  • Health Informatics
  • Machine Learning
  • Public Health

Background:

  • International Classification of Diseases, 10th Revision (ICD-10) codes are vital for clinical care and research.
  • Existing stroke detection algorithms lack generalizability across diverse racial groups.
  • Developing equitable digital health tools for stroke surveillance is crucial.

Purpose of the Study:

  • To develop and validate an accurate and fair acute ischemic stroke (AIS) detection algorithm using ICD-10 codes.
  • To assess the algorithm's performance across diverse racial and ethnic subgroups.
  • To establish a scalable and cost-efficient tool for stroke surveillance and population health.

Main Methods:

  • Developed a Classification and Regression Tree (CART) supervised machine learning model.
  • Utilized diagnostic and procedural ICD-10 codes from a diverse derivation cohort.
  • Externally validated the model in a separate institution serving vulnerable communities.

Main Results:

  • The CART model achieved high accuracy in the derivation cohort (sensitivity 96%, specificity 90%).
  • External validation demonstrated strong performance (sensitivity 89%, specificity 95%, κ=0.84).
  • Comparable performance was observed across sex and Black, Hispanic, and White subgroups, with lower accuracy in Asians.

Conclusions:

  • CART-based algorithms can accurately detect AIS using ICD-10 data while promoting social fairness.
  • The algorithm's reproducibility across diverse populations supports its use in clinical care and surveillance.
  • Ongoing fairness evaluation is necessary, as performance varied by race/ethnicity.