DeepAI AI Chat
Log In Sign Up

A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients

02/21/2022
by   Zhijun Zeng, et al.
3

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

11/24/2017

Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC

Multivariate techniques based on engineered features have found wide ado...
10/25/2019

Machine Translation from Natural Language to Code using Long-Short Term Memory

Making computer programming language more understandable and easy for th...
01/11/2021

Predicting Patient Outcomes with Graph Representation Learning

Recent work on predicting patient outcomes in the Intensive Care Unit (I...
12/05/2020

Bidirectional recurrent neural networks for seismic event detection

Real time, accurate passive seismic event detection is a critical safety...
11/04/2017

Predicting Discharge Medications at Admission Time Based on Deep Learning

Predicting discharge medications right after a patient being admitted is...
06/29/2020

Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks

The pressure of ever-increasing patient demand and budget restrictions m...