Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

ABSTRACT:

Objectives

The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE) in an individual patient.

Background

Patient similarity analysis is an evolving paradigm for precision medicine in which patients are clustered or classified based on their similarities in several clinical features.

Methods

A retrospective cohort of 866 patients was used to develop a network architecture using 9 echocardiographic features of LV structure and function. The data for 468 patients from 2 prospective cohort registries were then added to test the model's generalizability.

Results

The map of cross-sectiona

SUBMITTER: Tokodi M 

PROVIDER: S-EPMC7556337 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature