A long short-temory relation network for real-time prediction of patient-specific ventilator parameters

Xihe Qiu; Xiaoyu Tan; Chenghao Wang; Shaotao Chen; Bin Du; Jingjing Huang; Xihe Qiu; Xiaoyu Tan; Chenghao Wang; Shaotao Chen; Bin Du; Jingjing Huang

doi:10.3934/mbe.2023660

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14756-14776. doi: 10.3934/mbe.2023660

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Research article

A long short-temory relation network for real-time prediction of patient-specific ventilator parameters

1.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
INF Technology (Shanghai) Company Limited, Shanghai 201203, China
3.
Yanshan Electronics of Beijing, Beijing 100192, China
4.
ENT institute and Department of Otorhinolaryngology, Fudan University, Shanghai 200031, China
5.
Shanghai Municipal Key Clinical Specialty, Shanghai 200031, China

Academic Editor: Vladimir Mityushev

Received: 10 April 2023 Revised: 16 May 2023 Accepted: 30 May 2023 Published: 07 July 2023

Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment.
- neural networks,
- ventilator parameter prediction,
- machine learning,
- important feature selection,
- long short-term memory
Citation: Xihe Qiu, Xiaoyu Tan, Chenghao Wang, Shaotao Chen, Bin Du, Jingjing Huang. A long short-temory relation network for real-time prediction of patient-specific ventilator parameters[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14756-14776. doi: 10.3934/mbe.2023660

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Abstract

Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment.

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