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

Authors

Jingming Liu¹, Minghui Gong², Wei Guo¹, Chunping Li^{2, *,} , Hui Wang³, Shuai Zhang³, Christopher Nugent³

¹Emergency Department in Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

²School of Software, Tsinghua University, Beijing 100084, China

³Faculty of Computing, Ulster University Jordanstown, Northern Ireland BT37 0QB, UK

^*Corresponding author. Email: cli@tsinghua.edu.cn

DOI

10.2991/ijcis.d.200512.001How to use a DOI?

Keywords

Time series data; LSTM; Clustering; Sepsis; Blood pressure regulation

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.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jingming Liu
AU  - Minghui Gong
AU  - Wei Guo
AU  - Chunping Li
AU  - Hui Wang
AU  - Shuai Zhang
AU  - Christopher Nugent
PY  - 2020
DA  - 2020/06/29
TI  - Dose Regulation Model of Norepinephrine Based on LSTM Network and Clustering Analysis in Sepsis
JO  - International Journal of Computational Intelligence Systems
SP  - 717
EP  - 726
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200512.001
DO  - 10.2991/ijcis.d.200512.001
ID  - Liu2020
ER  -