Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data.

ABSTRACT: As a leading cause of death and morbidity, heart failure (HF) is responsible for a large portion of healthcare and disability costs worldwide. Current approaches to define specific HF subpopulations may fail to account for the diversity of etiologies, comorbidities, and factors driving disease progression, and therefore have limited value for clinical decision making and development of novel therapies. Here we present a novel and data-driven approach to understand and characterize the real-world manifestation of HF by clustering disease and symptom-related clinical concepts (complaints) captured from unstructured electronic health record clinical notes. We used natural language processing to construct vectorized representations of patient complaints followed by clustering to group HF patie

SUBMITTER: Nagamine T 

PROVIDER: S-EPMC7721729 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature