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

arXiv: https://arxiv.org/abs/2202.10921 · HTML: https://mlsanity.com/paper/2202.10921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)

## Built on by (most cited first, 1 of 1)

- [Two-step interpretable modeling of Intensive Care Acquired Infections](https://mlsanity.com/paper/2301.11146/) (2023)

## Abstract (arXiv)

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.