Predicting medical events and ICU requirements using a multimodal multiobjective transformer network

Abstract

Effective utilization of premium hospital resources such as intensive care unit (ICU), operating theater (OT), mechanical ventilator, endotracheal tube, and so on plays a significant role in providing high-quality care to critically ill patients within reasonable costs. Non-availability of specialized resources can lead to dire consequences for such patients, and in the worst case, may even turn out to be fatal. However, these resources cannot be kept idle, as they are expensive to maintain. Therefore, one of the core functions of hospital management is targeted at planning and managing these critical resources in order to provide efficient and effective health-care services to the end-users. Predictive technologies play a big role in this. In this article, we present methods for predicting the length of stay in ICU as well as the need for critical interventions for a patient based on the vital signs, laboratory measurements, and the nursing notes of the patient prepared within the first 24 h of ICU stay. The model has been built and cross-validated on the publicly available Medical Information Mart for Intensive Care (MIMIC-III v1.4) data set. We show that the proposed model performs way better than most of the earlier models in the prediction of ICU stay, which had used patient vitals primarily. Experimental results also demonstrate the advantage of using a multiobjective model over independent models for the prediction of ICU stay and critical interventions. The proposed model uses Local Interpretable Model-agnostic Explanations (LIME) that help in identifying the features responsible for predictive decisions. This is very useful in building trust and confidence in the prediction model among clinical practitioners.