HF patients correspond to the ICD-9 codes.

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（Heart Failure, HF）是各类心脏疾病进展至终末期的临床表现。心力衰竭患者的预后死亡率存在高度异质性，波动范围为5%至75%。评估心力衰竭患者的全因死亡率，是规避患者死亡、改善其健康状况的重要手段。但在实际应用中，机器学习模型在处理存在缺失值、高维度以及样本不均衡的心力衰竭数据集时，难以取得理想效果。为此，本研究提出一种深度学习系统：该系统首先设计了一种指示向量，用于标记数据值为真实观测值还是填充值，可快速解决缺失值问题并辅助拓展数据维度；随后采用不同卷积核尺寸的卷积神经网络（Convolutional Neural Network, CNN）提取特征信息；同时引入多头自注意力机制以获取全通道特征信息，这对提升系统性能至关重要；此外还引入焦点损失函数（Focal Loss）以更好地应对样本不均衡问题。本系统的实验数据来源于公开数据库MIMIC-III，共包含10311例患者的有效临床数据。所提出的深度学习系统可高效、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Deep SHAP对该深度学习模型进行可解释性分析，并得到排名前15的特征变量。这些特征变量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提供支撑。