Pressure Prediction System in Lung Circuit Using Deep Learning
Smart Innovation, Systems and Technologies
; 311:605-615, 2023.
Article
in English
| Scopus | ID: covidwho-2244769
ABSTRACT
A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Forecasting; Health care; Long short-term memory; Timing circuits; Ventilation; Acute respiratory distress syndrome; COVID-19 pandemic; Healthcare infrastructure; Mechanical ventilation; Prediction problem; Prediction systems; Pressure predictions; Pytorch; RNN; Sequence prediction; COVID-19; LSTM; Pressure prediction
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
Smart Innovation, Systems and Technologies
Year:
2023
Document Type:
Article
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