浙江医学2026，Vol.48Issue(5)：471-475,5.DOI:10.12056/j.issn.1006-2785.2026.48.5.2025-1340
基于多模态数据的机器学习对发生创伤性凝血病进展的预测价值分析及验证
Analysis and validation of the predictive value of machine learning based on multimodal data for the progression of traumatic co-agulopathy
摘要
Abstract
Objective To construct a predictive model for the progression of traumatic coagulopathy(TIC)using machine learning algorithms based on multimodal data,and to evaluate its predictive efficacy,so as to provide evidence for early identification of patients at high risk of TIC progression and timely intervention.Methods A convenience sampling method was adopted to select 734 trauma patients admitted to the Trauma Center of Beijing Jishuitan Hospital,Capital Medical University from January 2022 to December 2024 as the research subjects.Multimodal data including demographic information,clinical laboratory indicators,imaging data,and Injury Severity Score(ISS)of the patients were collected.Three machine learning predictive models(random forest,support vector machine,and neural network)were constructed using the scikit-learn library in Python 3.8 software,and the accuracy,precision,recall,F1 score,and AUC of each model were calculated.Results Univariate analysis showed that systolic blood pressure,diastolic blood pressure,hemoglobin(Hb),platelet count(PLT),prothrombin time(PT),D-dimer,ISS,complicated craniocerebral injury,complicated thoracic injury,and complicated abdominal injury were associated with the progression of TIC(all P＜0.05).Multivariate logistic regression analysis indicated that diastolic blood pressure,Hb,PLT,PT,D-dimer,and complicated thoracic injury were independent risk factors for predicting TIC progression(all P＜0.05).The AUC values of random forest,support vector machine,and neural network models were 0.812,0.856,and 0.893,respectively.Conclusion The constructed neural network model has the optimal efficacy in predicting TIC progression,which can effectively predict the risk of TIC progression in trauma patients.It helps clinical medical staff to early identify high-risk patients with TIC and provides decision support for timely intervention.关键词
创伤性凝血病/多模态数据/机器学习/预测价值Key words
Traumatic coagulopathy/Multimodal data/Machine learning/Predicted value引用本文复制引用
刘颖,吴俊..基于多模态数据的机器学习对发生创伤性凝血病进展的预测价值分析及验证[J].浙江医学,2026,48(5):471-475,5.基金项目
北京市自然科学基金-大兴创新联合基金项目(L246006) （L246006）
北京市卫生系统高层次公共卫生技术人才建设项目(02-18) （02-18）
