Mortality risk and health suggestions for critical patients using extended LSTM and CNN

Abstract

Intensive care units (ICUs) and their high mortality rate often require predictive tools that could help to determine at-risk patients in time and direct the interventions. The given project presents a deep learning system that merges Convolutional Neural Networks (CNN) with the Long Short Term Memory (LSTM) networks to better predict the risk of mortality at an early stage among critical patients in ICUs. Using a significant portion of the patient data such as the vital signs and laboratory results, the model can carry out constant risk analysis and it could prove superior to the conventional scoring systems. Health suggestion module is also incorporated to make suggestions on clinical interventions to be made on high risk patients thus assisting healthcare providers with their decision-making. Finally, the suggested direction will enhance the patient outcomes as it will allow delivering proactive medical care and, thus, distributing ICU resources more reasonably.