Dose Regulation Model of Norepinephrine Based on LSTM Network and Clustering Analysis in Sepsis

Jingming Liu, Minghui Gong, Wei Guo, Chunping Li, Hui Wang, Shuai Zhang, Chris Nugent

Research output: Contribution to journalArticle

Abstract

Sepsis is a life-threatening condition that arises when the body’s response to infection causes injury to its own tissues and organs. Despite the advancement of medical diagnosis and treatment technologies, the morbidity and mortality of sepsis are still relatively high. In this paper, a two-layer long short-term memory (LSTM) model is proposed to predict the dose of norepinephrine, in order to control the blood pressure of patients. The proposed modeling approach is evaluated using the MIMIC-III dataset, achieving higher performance.

Original languageEnglish
Pages (from-to)717-726
Number of pages10
JournalInternational Journal of Computational Intelligence Systems
Volume13
Issue number1
Early online date29 Jun 2020
DOIs
Publication statusE-pub ahead of print - 29 Jun 2020

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