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Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Authors :
Xue Zhou
Keijiro Nakamura
Naohiko Sahara
Masako Asami
Yasutake Toyoda
Yoshinari Enomoto
Hidehiko Hara
Mahito Noro
Kaoru Sugi
Masao Moroi
Masato Nakamura
Ming Huang
Xin Zhu
Source :
Life, Vol 12, Iss 6, p 776 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, p = 0.003), and 0.26 (95%CI 0.11–0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.

Details

Language :
English
ISSN :
20751729
Volume :
12
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Life
Publication Type :
Academic Journal
Accession number :
edsdoj.00d7866132d34c269dbd20a792500fb6
Document Type :
article
Full Text :
https://doi.org/10.3390/life12060776