# Ventilator pressure prediction using recurrent neural network

arXiv: https://arxiv.org/abs/2410.06552 · HTML: https://mlsanity.com/paper/2410.06552/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [Effect of Selection Format on LLM Performance](https://mlsanity.com/paper/2503.06926/) (2025)
- [Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models](https://mlsanity.com/paper/2412.18715/) (2024)

## Abstract (arXiv)

This paper presents a recurrent neural network approach to simulating mechanical ventilator pressure. The traditional mechanical ventilator has a control pressure that is monitored by a medical practitioner and can behave incorrectly if the proper pressure is not applied. This paper takes advantage of recent research and develops a simulator based on a deep sequence model to predict airway pressure in the respiratory circuit during the inspiratory phase of a breath given a time series of control parameters and lung attributes. This method demonstrates the effectiveness of neural network-based controllers in tracking pressure wave forms significantly better than the current industry standard and provides insights into the development of effective and robust pressure-controlled mechanical ventilators. The paper will measure as the mean absolute error between the predicted and actual pressures during the inspiratory phase of each breath.