Applying Risk Models on Patients with Unknown Predictor Values:An Incremental Learning Approach
In clinical practice,many patients may have unknown or missing values for some predictors,causing that the developed risk models cannot be directly applied on these patients.In this paper,we propose an incremental learning approach to apply a developed risk model on new patients with unknown predictor values,which imputes a patients unknown values based on his/her k-nearest neighbors(k-NN)from the incremental population.We perform a real world case study by developing a risk prediction model of stroke for patients with Type 2 diabetes mellitus from EHR data,and incrementally applying the risk model on a sequence of new patients.The experimental results show that our risk prediction model of stroke has good prediction performance.And the k-nearest neighbors based incremental learning approach for data imputation can gradually increase the prediction performance when the model is applied on new patients.
Cluster Analysis Theoretical Models Risk
Enliang Xu Xiang Li Jing Mei Shiwan Zhao Gang Hu Eryu Xia Haifeng Liu Guotong Xie Meilin Xu Xuejun Li
IBM Research-China,Beijing,China Pfizer Investment Co.Ltd.,Beijing,China Department of Endocrinology and Diabetes,the First Affiliated Hospital,Xiamen University,Xiamen,Chin
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
639-643
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）