Risk assessment of ICU patients through deep learning technique: A big data approach

Abstract

Background: Intensive Care Unit (ICU) patients are exposed to various medications, especially during infusion, and the amount of infusion drugs and the rate of their application may negatively affect their health status. A deep learning model can monitor a patient's continuous reaction to tranquillizer therapy, analyze the treatment plans of experts to avoid severe situations such as reverse medication associations, work with a convenient mediator, and change the treatment plans of specialists as needed. Methods: Generally, patients' treatment histories are linked together via a period grouping connection, which is usually burdened by missing information. Displaying time-succession via Repetitive Neural Organization (RNO) is the best available solution. However, it's possible that a patient's treatment may be prolonged, which RNN may not be able to demonstrate in this manner. Results: We propose the use of the LSTM-RNN driven by heterogeneous medicine events to predict the patient's outcome, as well as the Regular Language Handling and Gaussian Cycle, which can handle boisterous, deficient, inadequate, heterogeneous, and unevenly tested prescription records of patients while addressing the missing value issue using a piece-based Gaussian cycle. Conclusions: We emphasize the semantic relevance of every medication event and the grouping of drug events on patients in our study. We will focus specifically on LSTM-RNN and Phased LSTM-RNN for showing treatment results and information attribution using bit-based Gaussian cycles. We worked on Staged LSTM-RNN.

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