Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

Abstract

Background: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial intelligence (AI) machine learning algorithms.

Method: A retrospective analysis was conducted on electronic medical records of SICH patients aged 20 and above, admitted to Chi Mei Medical Center's intensive care unit between January 2016 and December 2021. The study utilized 37 features related to mortality. Predictive models were developed using logistic regression, Random forest, LightGBM, XGBoost, and Multi-layer Perceptron (MLP), with assessments of feature importance, and Area under the curve (AUC).

Results: A total of 1451 SICH patients were enrolled. Factors associated with mortality included lower initial GCS scores (p < 0.001), pupillary changes (P < 0.001), kidney disease (p < 0.001), and respiratory failure requiring intubation (p < 0.001). Negative correlations were observed between mortality and pupil light reflexes, as well as GCS components E(r=-0.4602), V (r=-0.4132), M(r=-0.4082). Positive correlations were identified with vasopressors (r = 0.4464), FiO2 (r = 0.3901), and sedative-hypnotic drugs (r = 0.1178). XGBoost demonstrated the best predictive performance (AUC = 0.913), outperforming LR (0.899), RF (0.905), LightGBM (0.909), and MLP (0.892). The XGBoost model, utilizing both 18 and 36 features, continues to outperform both the Acute Physiology and Chronic Health Evaluation (APACHE II) (p < 0.001) and Sequential Organ Failure Assessment (SOFA) scoring systems (p < 0.001).

Conclusion: This study successfully developed an AI mortality prediction model for SICH patients, with XGBoost exhibiting superior performance. The model, incorporating 18 key features, has been integrated into clinical practice assisting clinicians in treatment decisions and communication with patients' families.