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

doi:10.2991/ijcis.d.200512.001

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

Tsinghua University

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

154 Downloads (Pure)

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 language	English
Pages (from-to)	717-726
Number of pages	10
Journal	International Journal of Computational Intelligence Systems
Volume	13
Issue number	1
Early online date	29 Jun 2020
DOIs	https://doi.org/10.2991/ijcis.d.200512.001
Publication status	Published online - 29 Jun 2020

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SDG 3 Good Health and Well-being

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10.2991/ijcis.d.200512.001Licence: CC BY-NC

IJCIS_Publication_VersionFinal published version, 6.62 MBLicence: CC BY-NC

Cite this

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abstract = "Sepsis is a life-threatening condition that arises when the body{\textquoteright}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.",

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AU - Liu, Jingming

AU - Gong, Minghui

AU - Guo, Wei

AU - Li, Chunping

AU - Wang, Hui

AU - Zhang, Shuai

AU - Nugent, Chris

PY - 2020/6/29

Y1 - 2020/6/29

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85087108239

U2 - 10.2991/ijcis.d.200512.001

DO - 10.2991/ijcis.d.200512.001

M3 - Article

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JO - International Journal of Computational Intelligence Systems

JF - International Journal of Computational Intelligence Systems

IS - 1

ER -

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

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