| Autor: |
Giri J; Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India., Al-Lohedan HA; Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia., Mohammad F; Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia., Soleiman AA; Department of Chemistry, College of Science, Southern University and A&M College, Baton Rouge, LA 70813, USA., Chadge R; Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India., Mahatme C; Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India., Sunheriya N; Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India., Giri P; Laxminarayan Institute of Technology, Nagpur 440033, India., Mutyarapwar D; Tata Consultancy Services, Nagpur 441108, India., Dhapke S; Cognizant, Hyderabad 500019, India. |
| Abstrakt: |
Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches. |
