Visweswaran Shyam, Cooper Gregory F
Center for Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Pennsylvania, USA.
AMIA Annu Symp Proc. 2005;2005:759-63.
We investigated two patient-specific and four population-wide machine learning methods for predicting dire outcomes in community acquired pneumonia (CAP) patients. Predicting dire outcomes in CAP patients can significantly influence the decision about whether to admit the patient to the hospital or to treat the patient at home. Population-wide methods induce models that are trained to perform well on average on all future cases. In contrast, patient-specific methods specifically induce a model for a particular patient case. We trained the models on a set of 1601 patient cases and evaluated them on a separate set of 686 cases. One patient-specific method performed better than the population-wide methods when evaluated within a clinically relevant range of the ROC curve. Our study provides support for patient-specific methods being a promising approach for making clinical predictions.
我们研究了两种针对特定患者的机器学习方法和四种针对全体人群的机器学习方法，用于预测社区获得性肺炎（CAP）患者的严重后果。预测CAP患者的严重后果会显著影响关于是否将患者收治入院或在家治疗的决策。针对全体人群的方法所生成的模型，是经过训练以在所有未来病例上平均表现良好。相比之下，针对特定患者的方法则专门为特定患者病例生成一个模型。我们在一组1601例患者病例上训练这些模型，并在另一组686例病例上对其进行评估。当在ROC曲线的临床相关范围内进行评估时，一种针对特定患者的方法比针对全体人群的方法表现更好。我们的研究为针对特定患者的方法作为一种有前景的临床预测方法提供了支持。
