Machine-learning-based risk stratification for probability of dying in patients with basal ganglia hemorrhage

Sci Rep. 2022 Dec 5;12(1):21035. doi: 10.1038/s41598-022-25527-1.


To confirm whether machine learning algorithms (MLA) can achieve an effective risk stratification of dying within 7 days after basal ganglia hemorrhage (BGH). We collected patients with BGH admitted to Sichuan Provincial People's Hospital between August 2005 and August 2021. We developed standard ML-supervised models and fusion models to assess the prognostic risk of patients with BGH and compared them with the classical logistic regression model. We also use the SHAP algorithm to provide clinical interpretability. 1383 patients with BGH were included and divided into the conservative treatment group (CTG) and surgical treatment group (STG). In CTG, the Stack model has the highest sensitivity (78.5%). In STG, Weight-Stack model achieves 58.6% sensitivity and 85.1% specificity, and XGBoost achieves 61.4% sensitivity and 82.4% specificity. The SHAP algorithm shows that the predicted preferred characteristics of the CTG are consciousness, hemorrhage volume, prehospital time, break into ventricles, brain herniation, intraoperative blood loss, and hsCRP were also added to the STG. XGBoost, Stack, and Weight-Stack models combined with easily available clinical data enable risk stratification of BGH patients with high performance. These ML classifiers could assist clinicians and families to identify risk states timely when emergency admission and offer medical care and nursing information.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Basal Ganglia Hemorrhage*
  • Humans
  • Logistic Models
  • Machine Learning*
  • Risk Assessment