We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model. We focus on short-term predictions of a set of ventilator parameters for the septic patient in emergency intensive care unit (EICU). The short-term predictability of the model provides attending physicians with early warnings to make timely adjustment to the treatment of the patient in the EICU. The patient specific deep learning model can be trained on any given critically ill patient, making it an intelligent aide for physicians to use in emergent medical situations.
@article{arxiv.2202.10921,
title = {A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients},
author = {Zhijun Zeng and Zhen Hou and Ting Li and Lei Deng and Jianguo Hou and Xinran Huang and Jun Li and Meirou Sun and Yunhan Wang and Qiyu Wu and Wenhao Zheng and Hua Jiang and Qi Wang},
journal= {arXiv preprint arXiv:2202.10921},
year = {2022}
}