Key result
The proposed deep learning system (DLS-MSM) effectively predicted heart failure mortality, achieving an AUC of 91.00% for 30-day mortality and demonstrating robustness against missing values and data imbalance.
Why the study?
Machine learning models struggle to achieve good results on missing values, high dimensions, and imbalanced data when evaluating all-cause mortality in heart failure patients.
Does a deep learning system (DLS-MSM) improve mortality prediction in heart failure patients compared to traditional machine learning models?
Observational (n=10,311)
No
Does a deep learning system (DLS-MSM) improve mortality prediction in heart failure patients compared to traditional machine learning models?
A novel deep learning system using CNN and multi-head self-attention with an indicator vector for missing values effectively predicts short- and long-term mortality in heart failure patients using electronic health records.
May aid HF mortality stratification in research settings; leaves open need for external validation before clinical use.
Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, ranging from 5% to 75%. Evaluating the all-cause mortality of HF patients is an important means to avoid death and positively affect the health of patients. But in fact, machine learning models are difficult to gain good results on missing values, high dimensions, and imbalances HF data. Therefore, a deep learning system is proposed. In this system, we propose an indicator vector to indicate whether the value is true or be padded, which fast solves the missing values and helps expand data dimensions. Then, we use a convolutional neural network with different kernel sizes to obtain the features information. And a multi-head self-attention mechanism is applied to gain whole channel information, which is essential for the system to improve performance. Besides, the focal loss function is introduced to deal with the imbalanced problem better. The experimental data of the system are from the public database MIMIC-III, containing valid data for 10311 patients. The proposed system effectively and fast predicts four death types: death within 30 days, death within 180 days, death within 365 days and death after 365 days. Our study uses Deep SHAP to interpret the deep learning model and obtains the top 15 characteristics. These characteristics further confirm the effectiveness and rationality of the system and help provide a better medical service.
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Li et al. (2023) conducted an observational in Heart failure (n=10,311). Deep Learning System based on Multi-head Self-attention Mechanism (DLS-MSM) vs. Traditional machine learning models (SVM, MLP, LR, RF, LGB, KNN) was evaluated on Mortality prediction accuracy (AUC) at 30 days. The proposed deep learning system (DLS-MSM) effectively predicted heart failure mortality, achieving an AUC of 91.00% for 30-day mortality and demonstrating robustness against missing values and data imbalance.
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