The role of waveform monitoring in Sepsis identification within the first hour of Intensive Care Unit stay

The latest extensive development of machine learning models in healthcare, and in particular their application to data from the intensive care unit (ICU), is directed towards the main objective to help clinicians in making more timely diagnoses and efficient decisions. Many studies have been focused on the identification of Sepsis in a complex environment such as the ICU by using the data collected in electronic health records. However, only a few studies have investigated associations between the patients' continuously monitored vital signs and their Sepsis status. This work aims at demonstrating that machine learning algorithms considering measures extracted from 103 patients from the publicly available MIMIC-III clinical and waveform databases are able to adequately identify Sepsis just within the first hour of stay in the ICU. A bagged tree classifier showed AUC=0.86, Specificity=0.85 and Sensitivity=0.86 on the test set, when trained using only the information extracted from the recorded electrocardiogram and arterial blood pressure waveforms, showing that the information coming from waveform monitoring may help in detecting Sepsis within the first hour of ICU stay