Sequential Data-Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development.

ABSTRACT:

Background

Sequential information in electronic medical records is valuable and helpful for patient outcome prediction but is rarely used for patient similarity measurement because of its unevenness, irregularity, and heterogeneity.

Objective

We aimed to develop a patient similarity framework for patient outcome prediction that makes use of sequential and cross-sectional information in electronic medical record systems.

Methods

Sequence similarity was calculated from timestamped event sequences using edit distance, and trend similarity was calculated from time series using dynamic time warping and Haar decomposition. We also extracted cross-sectional information, namely, demographic, laboratory test, and radiological report data, for additional similarity calculations

SUBMITTER: Wang N 

PROVIDER: S-EPMC8778569 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature