Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage

Tang, Lei, Li, Ye, Zhang, Ji, Zhang, Feng, Tang, Qiaoling, Zhang, Xiangbin, Wang, Sai, Zhang, Yupeng, Ma, Siyuan, Liu, Ran, Chen, Lei, Ma, Junyi, Zou, Xuelun, Yao, Tianxing, Tang, Rongmei, Zhou, Huifang, Wu, Lianxu, Yi, Yexiang, Zeng, Yi, Wang, Duolao ORCID: https://orcid.org/0000-0003-2788-2464 and Zhang, Le (2025) 'Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage'. Scientific Reports, Vol 15, Issue 1, p. 16326.

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Abstract

Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in predicting sepsis risk in intensive care units (ICUs) patients with ICH. We conducted a retrospective analysis on ICH patients using the MIMIC-IV database, randomly dividing them into training and validation cohorts. We identified sepsis prognostic factors using Least Absolute Shrinkage and Selection Operator (LASSO) and backward stepwise logistic regression. Several machine learning algorithms were developed and assessed for predictive accuracy, with external validation performed using the eICU Collaborative Research Database (eICU-CRD). We analyzed 2,214 patients, including 1,550 in the training set, 664 in the validation set, and 513 for external validation using the eICU-CRD. The Random Forest (RF) model outperformed others, achieving Area Under the Curves (AUCs) of 0.912 in training, 0.832 in internal validation, and 0.798 in external validation. Neural Network and Logistic Regression models recorded training AUCs of 0.840 and 0.804, respectively. ML models, especially the RF model, effectively predict sepsis in ICU patients with ICH, enabling early identification and management of high-risk cases.

Item Type:	Article
Subjects:	W General Medicine. Health Professions > W 82 Biomedical technology (General) WC Communicable Diseases > Infection. Bacterial Infections > Bacterial Infections > WC 240 Bacteremia. Sepsis. Toxemias
Faculty: Department:	Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI):	https://doi.org/10.1038/s41598-025-99431-9
SWORD Depositor:	JISC Pubrouter
Depositing User:	JISC Pubrouter
Date Deposited:	30 May 2025 08:15
Last Modified:	30 May 2025 08:15
URI:	https://archive.lstmed.ac.uk/id/eprint/26691

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