RPMF: In-hospital Mortality Risk Prediction based on Multimodal Fusion

In predicting mortality and disease risk, deep learning in clinical decision-support technology to analyze structured electronic health
records EHR has been a highly scrutinized research area. However,
despite the abundance of narrative clinical diagnostic records and ICU
physiological indicators, we still believe there are two shortcomings in
current disease risk prediction efforts. On the one hand, current models
fail to utilize the available data fully and lack comprehensive modeling
of patient characteristics. On the other hand, existing studies have not
effectively captured potential correlations between multimodal data. In
this paper, we introduced a pioneering design concept based on the initial health state of the patient, which involves considering the patient’s
current health status characterized by disease information as a key element in sequence modeling. In addition, we have innovatively adopted
the Informer model for processing time-series data of physiological indicators of ICU patients. More critically, we developed a multimodal feature interaction module that captures the interrelationships between different data modalities. Extensive experiments on real-world datasets show that our model significantly outperforms existing models, fully validating the efficiency of our proposed model.