Risk assessment plays a crucial role in reducing mortality and optimizing healthcare resource utilization in cardiac care units (CCU). However, the criteria for admitting cardiac patients to CCU often rely on subjective judgments by cardiologists. Therefore, our study aimed to evaluate risk by predicting patient prognosis at discharge using multimodal learning, incorporating electrocardiography (ECG), a routine examination conducted upon CCU admission, and clinical data. We included 892 survivors and 289 non-survivors, who were admitted to the CCU between 2008 and 2020, with prognosis at discharge as the endpoint. The 12-lead ECGs obtained at admission were converted into images, and ECG features were extracted using a convolutional neural network. These features were then combined with clinical data, comprising 22 items, to predict the prognosis at discharge using gradient boosting. Additionally, the contribution of each feature was calculated using SHapley Additive exPlanations. The 10-fold cross-validation demonstrated an improved accuracy, with an AUC of 0.889, compared to using ECG and clinical data independently. Notably, the machine learning model exhibited a particular focus on myocardial troponin and eGFR as influential factors. These findings suggest that our proposed method holds promise for enhancing risk assessment in CCU patients.
