A Prediction Model of In-Patient Deteriorations Based on Passive Vital Signs Monitoring Technology

Abstract

Lately, many health systems accelerated their initiatives of advanced remote monitoring systems. Moving to an unattended environment requires overcoming patients' compliance issues and demonstrating the effectiveness of remote monitoring technology. Current Early Warning Scores detection of deterioration, commonly based on spot check EMR data, demonstrates low translational impact from one facility to another. In this study we used vitals collected passively by a sensor, to build a Machine Learning model for timely prediction of deteriorating patients, within 24-hours of their transfer to ICU or death. Time series features, such as trends and vitals' variability were used in conjunction with age & comorbidity data. Evaluating the model yielded an AUROC of 0.81 on data from an inpatient setting, and an AUROC of 0.88 on an independent test set from a COVID-19 unit. The suggested model, based on passive measurement technology, performs equally well as models based on EMR that include nurse inputs. Applying the model on other acute settings (such as a COVID-19 unit) showed similar performance, increasing confidence of its robustness and transferability. The model performance combined with the fact that it does not require human compliance, makes it a good candidate for future testing on home settings.