Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine
Xue Zhou1, Keijiro Nakamura2, Naohiko Sahara2
1Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu 965-8580, Japan.
Life (Basel, Switzerland)
|June 24, 2022
Summary
Machine learning identified three heart failure patient phenotypes with distinct mortality risks. This approach aids in personalized medicine by classifying new patients and guiding tailored management strategies.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Precision medicine requires identifying patient subgroups with different prognoses.
- Heart failure (HF) patient stratification is crucial for effective management.
- Machine learning (ML) offers potential for discovering prognostic phenotypes.
Purpose of the Study:
- To explore patient phenotypes in heart failure (HF) linked to mortality risk.
- To develop and validate ML models for classifying these prognostic phenotypes.
- To interpret the factors influencing phenotype classification.
Main Methods:
- Unsupervised ML was used to identify phenotypes in a derivation cohort (n=562).
- Supervised ML models were trained for phenotype classification and validated on an independent cohort (n=168).
- Shapley additive explanations (SHAP) were employed for model interpretability.
Main Results:
- Three distinct phenotypes with stratified mortality risks (high, intermediate, low) were identified.
- Significant differences in survival curves and hazard ratios were observed among phenotypes.
- Random forest models achieved high AUCs (0.721-0.815) for phenotype classification, with age and creatinine clearance as key predictors.
Conclusions:
- ML effectively identifies prognostic patient phenotypes in heart failure.
- This facilitates personalized patient management based on predicted mortality risk.
- The identified phenotypes and predictors can guide clinical decision-making.
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