What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice.

ABSTRACT:

Introduction

The aim of this work was to understand between-hospital variation in thrombolysis use among emergency stroke admissions in England and Wales.

Patients

A total of 88,928 patients who arrived at all 132 emergency stroke hospitals in England Wales within 4 h of stroke onset, from 2016 to 2018.

Methods

Machine learning was applied to the Sentinel Stroke National Audit Programme (SSNAP) data set, to learn which patients in each hospital would likely receive thrombolysis. We used XGBoost machine learning models, coupled with a SHAP model for explainability; Shapley (SHAP) values, providing estimates of how patient features, and hospital identity, influence the odds of receiving thrombolysis.

Results

Thrombolysis use in patients arriving within 4 h of kno

SUBMITTER: Pearn K 

PROVIDER: S-EPMC10683721 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature