Real-time Patient Response Forecasting in ICU: A Robust Model Driven by LSTM and Advanced Data Processing Approaches

People who are in an ICU (intensive care unit) are especially vulnerable to the adverse consequences of drugs, especially infusion drugs, because the rate and dose of the medication can make their situation worse. A model that predicts death can keep an eye on how patients respond to drug therapies in real time. It also checks doctors' therapies to make sure they don't cause major problems like bad interactions between drugs. This lets people step in quickly and change treatment plans as needed. Patients' care usually goes in a certain order over time, which can be hard to figure out when there is missing information in their healthcare history. In this particular setting, we propose the deployment of a Long Short-Term Memory (LSTM) model that is driven by diverse drug events in order to facilitate the prediction of the outcomes for patients. Furthermore, we suggest the combination of Text processing approaches and Gaussian Techniques (GT) approaches, which are intended to effectively handle the issues relating to noisy, insufficient, sparse, inconsistent, and sporadically sampled medical records of patients. These challenges would be effectively managed by integrating these techniques. Our method lays a large amount of emphasis on recording the semantic meaning of each pharmaceutical event as well as the ordered sequence in which these occurrences occur for patients. In addition, we provide a solution to the problem of missing values by utilizing a kernel-based Gaussian technique within the framework we provide. In order to demonstrate that our proposed strategy is effective, we carried out a thorough performance comparison with a number of different ways that are considered to be state-of-the-art. Because of these findings, it is clear that the LSTM model, when incorporated into the framework that we have established, regularly produces solid results when it comes to forecasting patient responses.