基于机器学习构建腹膜炎患者发生脓毒血症的预测模型
Construction of a Predictive Model for Sepsis in Peritonitis Patients Based on Machine Learning
DOI: 10.12677/acm.2025.152528, PDF,   
作者: 何 锟, 金能烈, 聂 飚*：暨南大学附属第一医院消化内科，广东 广州
关键词: 腹膜炎；脓毒血症；机器学习；预测模型；Peritonitis； Sepsis； Machine Learning； Predictive Model
摘要: 腹膜炎是全球重症监护病房患者败血症的第二大死亡原因，脓毒血症的早期预测对于及时干预并最终改善预后至关重要。本研究基于新型的机器学习算法，建立并验证腹膜炎患者发展为脓毒血症的预测模型，研究结果提示机器学习模型可以成为预测腹膜炎患者预测脓毒血症的可靠工具，并且，随机森林算法模型具有最佳的预测性能，这种机器学习方法可用于帮助临床医生对于高风险因素的认识并早期干预以降低死亡率。
Abstract: Peritonitis is the second leading cause of sepsis-related mortality in intensive care unit (ICU) patients worldwide. Early prediction of sepsis is critical for timely intervention and ultimately improving prognosis. This study established and validated a predictive model for the development of sepsis in peritonitis patients using novel machine learning algorithms. The findings suggest that machine learning models can be a reliable tool for predicting sepsis in peritonitis patients. Among them, the random forest algorithm model showed the best predictive performance. This machine learning approach can help clinicians recognize high-risk factors and intervene early to reduce mortality.
文章引用：何锟, 金能烈, 聂飚. 基于机器学习构建腹膜炎患者发生脓毒血症的预测模型[J]. 临床医学进展, 2025, 15(2): 1707-1717. https://doi.org/10.12677/acm.2025.152528

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