摘要
目的/意义 利用基于多源数据融合的机器学习算法,预测缺血性脑卒中患者的临床药物治疗风险。方法/过程 基于国际脑卒中试验数据集,融合患者人口统计学、生命体征检查及临床药物治疗数据,利用随机森林、逻辑回归和梯度提升决策树算法预测用药风险。结果/结论 3种算法在预测性能方面都表现较好,其中梯度提升决策树的召回率达到91.6%,曲线下面积为0.832,效果最佳。多源数据融合的机器学习算法在缺血性脑卒中用药风险预警中具有良好适用性。
Purpose/Significance To predict the risk of medication for ischemic stroke patients using a machine learning algorithm based on multi-source data fusion.Method/Process The study is based on the international stroke trial datasets.By fusing features of patient demographics,vital sign examination and medication data,it predicts medication risks using random forest,logistic regression and gradient boosting decision tree(GBDT)algorithms.Result/Conclusion The results show that three algorithms performe well,with the best recall of 91.6%and area under the curve is 0.832 for GBDT algorithm.The machine learning algorithms with multi-source data fusion has good applicability in ischemic stroke medication risk prediction.
作者
王文卓
秦秋莉
WANG Wenzhuo;QIN Qiuli(Beijing Jiaotong University,Beijing 100091,China)
出处
《医学信息学杂志》
CAS
2023年第10期50-55,共6页
Journal of Medical Informatics
关键词
多源数据融合
缺血性脑卒中
风险预测
智慧医疗
multi-source data fusion
ischemic stroke
risk prediction
smart medical care