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

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 140

SUBMITTER: Wang N 

PROVIDER: S-EPMC6788002 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature