Invasive mechanical ventilation probability estimation using machine learning methods based on non-invasive parameters

Timely and accurate prediction of the requirement for invasive mechanical ventilation (IMV) can reduce patient mortality. Existing methods (traditional risk adjustment algorithms, clinical observation, et.) use laboratory parameters requiring specialized biochemical analysis, which is difficult to obtain in the pre-hospital emergency setting and does not accurately predict the requirement for IMV. In this study, 20 non-invasive parameters including patient demographic parameters, physiological parameters, Glasgow score and ventilator parameters, were extracted from the Medical Information Mart for Intensive Care III (MIMIC III) database. A real-time early warning model of IMV requirement was developed using classical seven machine learning methods in different categories and compared with two traditional risk adjustment algorithms. The prediction results using Lightgbm were 0.917 (95 %CI:0.914–0.922) for area under receiver operating characteristic curve (AUC) and 0.853 for accuracy (ACC) (95 %CI:0.850–0.856), outperforming the traditional risk adjustment algorithm, which were 0.615 and 0.533 respectively. The addition of invasive parameters increased the AUC value of the model by 0.009. A real-time early warning algorithm was developed in this paper for IMV requirement based on non-invasive parameters using seven learning methods, which proved to be superior to the traditional risk adjustment algorithm. Using real-time clinical data, the proposed algorithm can calculate current and future requirement for IMV requirement at any point in time during the stay of a patient in the ICU. Finally, it provides technical support for a wide range of applications in remote areas and disaster sites, where invasive parameters are unavailable.