A Comparative Study on Predication of Appropriate Mechanical Ventilation Mode through Machine Learning Approach

Abstract

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.

Comparative assessment of supervised machine learning algorithms for predicting geometric characteristics of laser cladded inconel 718

“…Drawing upon relevant literature and the authors' own investigations into mainstream machine learning techniques, this paper employs a selection of algorithms, including K-Nearest Neighbors (KNN), Backpropagation Neural Network (BP), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Trees, and XGBoost [3,20,[26][27][28][29][30][31]. The objective is to compare their predictive performance in the context of this study.…”

“…According to literature research, commonly used ML algorithms in establishing prediction models for laser cladding include Back-propagation Neural Network (BP), SVR, DTR, RFR, GBR, etc. [3,20,[26][27][28][29][30][31].…”

Machine learning algorithm for ventilator mode selection, pressure and volume control

“…Deep learning approaches have shown a great capacity to predict and identify PVA. However, these techniques are constrained since model training requires a large quantity of labelled information, which might impede practical application [ 22 , 23 ]. A framework for transfer learning has been created according to pre-defined neural model tailored particularly for asynchrony support using limited data records.…”