Group and number of experiments.

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.

心力衰竭（HF）是各类心脏疾病进展至终末期的阶段。心力衰竭预后患者的病死率差异悬殊，区间跨度为5%至75%。评估心力衰竭患者的全因死亡率，是降低患者死亡风险、改善其健康状况的重要手段。但实际上，机器学习模型在处理存在缺失值、高维度且类别不平衡的心力衰竭数据集时，难以取得理想效果。为此，本研究提出一款深度学习系统。该系统中，我们设计了一种指示向量，用于标记数值为真实值还是填充值，可快速解决缺失值问题并辅助扩展数据维度；随后采用不同卷积核尺寸的卷积神经网络提取特征信息；同时引入多头自注意力机制以捕获全局通道信息，这对提升系统性能至关重要。此外，我们引入焦点损失函数（Focal Loss）以更好地处理类别不平衡问题。本系统的实验数据源自公开数据库MIMIC-III，涵盖10311名患者的有效临床数据。所提出的系统可高效、快速地预测四类死亡结局：30天内死亡、180天内死亡、365天内死亡以及365天后死亡。本研究采用深度SHAP（Deep SHAP）对深度学习模型进行可解释性分析，并提取出排名前15的特征。这些特征进一步验证了本系统的有效性与合理性，可为临床提供更优质的医疗服务支撑。