| 基于多源数据融合的缺血性脑卒中用药风险预警研究 |
| 修订日期：2023-05-04 点此下载全文 |
| 引用本文：王文卓,秦秋莉.基于多源数据融合的缺血性脑卒中用药风险预警研究[J].医学信息学杂志,2023,44(10):50~55 |
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| 中文摘要: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。 |
| 中文关键词:多源数据融合 缺血性脑卒中 风险预测 智慧医疗 |
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| Study on Predicting the Risk of Ischemic Stroke Medication Treatment Based on Multi-source Data Fusion |
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| Abstract: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. |
| keywords:multi-source data fusion ischemic stroke risk prediction smart medical care |
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