Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction
Zhibo Wang1, Xin Chen2, Xi Tan2
1Merck & Co., Inc., Kenilworth, NJ, USA; College of Engineering and Computer Science, University of Central Florida, Orlando, FL, USA.
Journal of Health Economics and Outcomes Research
|August 20, 2021
Summary
Deep learning models effectively predict heart failure hospitalizations and readmissions in heart failure with reduced ejection fraction patients. This approach outperforms traditional methods, offering a promising tool for identifying high-risk individuals.
Area of Science:
- Cardiology
- Biomedical Informatics
- Artificial Intelligence
Background:
- Deep learning (DL) is not yet well-established for identifying high-risk heart failure (HF) patients.
- Predictive models are crucial for managing patients with heart failure with reduced ejection fraction (HFrEF).
Purpose of the Study:
- To utilize DL models for predicting hospitalizations, worsening HF events, and 30/90-day readmissions in HFrEF patients.
- To compare DL model performance against traditional machine learning algorithms.
Main Methods:
- Analysis of adult HFrEF patient data from IBM MarketScan databases (2015-2017).
- Implementation of a sequential model architecture using bi-directional long short-term memory (Bi-LSTM) layers.
- Comparison with logistic regression, random forest, and XGBoost models, assessing performance via AUC, precision, and recall.
Main Results:
- DL models achieved high AUCs (up to 0.977) for predicting HF hospitalizations and worsening HF events without a buffer window.
- AUCs for 30- and 90-day readmissions were 0.597 and 0.614, respectively; 0.861 for 90-day readmission in 18-64 age group.
- DL models consistently outperformed traditional machine learning models across all assessed outcomes.
Conclusions:
- A DL approach using Bi-LSTM is a feasible and effective tool for predicting HF-related outcomes.
- This technology can aid in identifying high-risk HFrEF patients for targeted interventions.
- Further development is needed to address data limitations and enhance predictive accuracy.
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