Integrating Multimodal EHR Data for Mortality Prediction in ICU Sepsis Patients.

Rapid and accurate prediction of mortality risk among intensive care unit (ICU) sepsis patients is crucial for timely intervention and improving patient outcomes. However, due to the multimodal and dynamic time-series nature of patient visit information and the limited data samples, it is challenging to obtain discriminative patient representations, leading to suboptimal mortality prediction results. To address this issue, we design a time-aware graph embedding attention model (TGAM) to integrate multimodal data and predict mortality in ICU sepsis patients. Our approach involves modeling and generating patient representations that encompass not only demographic information but also dynamic time-series data reflecting patient health status. Additionally, the graph convolutional network is used to obtain informative concept embeddings from medical ontologies, and an improved transformer is used to capture the temporal information of the patient's health status and handle missing values, overcoming the limitations of small samples. The experimental results on the MIMIC-III and MIMIC-IV datasets demonstrate that TGAM significantly improves prediction accuracy, with AUROC scores of 87.65% and 87.00% on the MIMIC-III and MIMIC-IV datasets, respectively, outperforming baseline models by over 5 percentage points. TGAM also achieves higher sensitivity, specificity, and AUPRC metrics, and lower Brier Score compared with baseline models, highlighting its effectiveness in identifying high-risk patients. These findings suggest that TGAM has the potential to become a valuable tool for identifying high-risk sepsis patients, enabling clinicians to make more informed and timely intervention decisions.