Unsupervised Learning to Subphenotype Heart Failure Patients from Electronic Health Records

Abstract

Heart failure (HF) is a deadly disease and its prevalence is slowly increasing. The sub-types of HF are currently mostly determined by the so-called ejection fraction (EF). In this work, we try to find novel subgroups of heart failure following a complete data-driven approach of clustering patients based on their electronic health records (EHRs). Using a validated phenotyping algorithm we were able to identify 14,334 adult patients with heart failure in our database. We derived embeddings of patients using two different strategies, one processing aggregated clinical features using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP), and one where we learn embeddings from the sequence of medical events using a long short-term memory (LSTM) autoencoder. Then we evaluated different clustering strategies like k-means and agglomerative hierarchical to derive the most informative subtypes. The results were compared based on different metrics such as silhouette coefficient and so on and also based on comparing outcomes such as hospitalization, EF etc. between the clusters. In the most promising result, we were able to identify 3 subclusters using the aggregated data approach in combination with UMAP as dimension reduction method and k-means as cluster method. Patients in cluster 1 had the lowest number of hospital days and comorbidities, while patients in cluster 3 had a significantly higher number of hospital days together with a higher prevalence of comorbidities such as chronic kidney disease and atrial fibrillation. Patients in cluster 2 had a high prevalence of drug allergies in their medical history.

M. Hackl and S. Datta—Contributed equally to this paper.