Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records.

ABSTRACT:

Background

A new learning-based patient similarity measurement was proposed to measure patients' similarity for heterogeneous electronic medical records (EMRs) data.

Methods

We first calculated feature-level similarities according to the features' attributes. A domain expert provided patient similarity scores of 30 randomly selected patients. These similarity scores and feature-level similarities for 30 patients comprised the labeled sample set, which was used for the semi-supervised learning algorithm to learn the patient-level similarities for all patients. Then we used the k-nearest neighbor (kNN) classifier to predict four liver conditions. The predictive performances were compared in four different situations. We also compared the performances between personalized kNN m

SUBMITTER: Wang N 

PROVIDER: S-EPMC8323210 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature