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

Xiaobing Huang, Shan Shan, Yousaf A Khan, Sultan Salem, Abdullah Mohamed, El-Awady Attia

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
27 Downloads (Pure)

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.
Original languageEnglish
Article number04044
Number of pages13
JournalJournal of Global Health
Volume12
Early online date30 May 2022
DOIs
Publication statusPublished - 6 Jul 2022

Bibliographical note

Open access journal which uses a CC-BY license.

Funding Information:
Funding: This research was supported by The National Science Foundation of China under Grant No.718945. Soni SR, Khunteta A, Gupta M. A review on intelligent methods used in medicine and life science. 2011.

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • Public Health, Environmental and Occupational Health
  • Health Policy

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