Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records

BioMedical Engineering OnLine

Xiaolu Fei¹  Lan Wei¹  Ni Wang²  Honglei Liu²  Hui Chen²  Yanqun Huang²  Xiangkun Zhao² 

[1] Information Center, Xuanwu Hospital, Capital Medical University;School of Biomedical Engineering, Capital Medical University;

关键词: Patient similarity;    Electronic medical records;    Personalized prediction;    Model performance;    Diabetes mellitus;   

DOI  :  10.1186/s12938-019-0718-2

来源: DOAJ

【 摘 要 】

Abstract Background Conventional risk prediction techniques may not be the most suitable approach for personalized prediction for individual patients. Therefore, individualized predictive modeling based on similar patients has emerged. This study aimed to propose a comprehensive measurement of patient similarity using real-world electronic medical records data, and evaluate the effectiveness of the individualized prediction of a patient’s diabetes status based on the patient similarity. Results When using no more than 30% of the whole training sample, the personalized predictive models outperformed corresponding traditional models built on randomly selected training samples of the same size as the personalized models (P < 0.001 for all). With only the top 1000 (10%), 700 (7%) and 1400 (14%) similar samples, personalized random forest, k-nearest neighbor and logistic regression models reached the globally optimal performance with the area under the receiver-operating characteristic (ROC) curve of 0.90, 0.82 and 0.89, respectively. Conclusions The proposed patient similarity measurement was effective when developing personalized predictive models. The successful application of patient similarity in predicting a patient’s diabetes status provided useful references for diagnostic decision-making support by investigating the evidence on similar patients.

【 授权许可】

Unknown