Lung pressure predictive model using LSTM: A deep learning techniques

Abstract

<p>The human body relies on controlled breathing to ensure oxygen reaches all cells while filtering out contaminants to protect the lungs. However, infections like the Delta virus and SARS-CoV2 (COVID-19) have led to Acute Respiratory Distress Syndrome (ARDS), requiring urgent medical care, including mechanical ventilation. The overwhelming number of patients has strained healthcare organizations and workers, necessitating advancements in automated healthcare technology. To address this challenge, we propose a novel solution to predict pressure in mechanical ventilation (MV) for various lung illnesses. The goal is to accurately predict the pressure within the respiratory circuit, which poses a challenging sequence prediction issue. To tackle this, we employ a cutting-edge deep learning approach known as Long Short-Term Memory (LSTM), which exhibits remarkable performance in selectively recalling patterns over time. While traditional recurrent neural networks (RNNs) can handle short-term patterns well, the introduced LSTM technique excels in managing complex sequence prediction problems. Comparing the proposed method with four existing algorithms, the researchers demonstrate that their approach achieves significantly higher accuracy. The impressively low error rate of 1.85 × 10<sup>−7</sup> showcases a substantial improvement over existing system. This groundbreaking advancement has the potential to alleviate the pressure on the current healthcare infrastructure and significantly improve care for patients in need of mechanical ventilation due to respiratory issues.</p>