Machine Learning Based Algorithms to Impute PaO 2 from SpO2 Values and Development of an Online Calculator
Abstract We created an online calculator using machine learning algorithms to impute the partial pressure of oxygen (PaO2)/fraction of delivered oxygen (FiO2) ratio using the non-invasive peripheral saturation of oxygen (SpO2) and compared the accuracy of the machine learning models we developed to previously published equations. We generated three machine learning algorithms (neural network, regression, and kernel-based methods) using 7 clinical variable features (N=9,900 ICU events) and subsequently 3 features (N=20,198 ICU events) as input into the models. Data from mechanically ventilated ICU patients were obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC III) database and used for analysis. Compared to seven features, three features (SpO2, FiO2 and PEEP) were sufficient to impute PaO2 from the SpO2. Any of the tested machine learning models enabled imputation of PaO2 from the SpO2 with lower error and showed greater accuracy in predicting PaO2/FiO2 < 150 compared to the previously published log-linear and non-linear equations. Imputation using data from an independent validation cohort of ICU patients (N = 133) from 2 hospitals within the University of Pittsburgh Medical Center (UPMC) showed greater accuracy with the neural network and kernel-based machine learning models compared to the previously published non-linear equation.