Continuous cardiorespiratory monitoring is a dominant source of predictive signal in machine learning for risk stratification and clinical decision support<sup> * </sup>
Oliver Monfredi, Jessica Keim‐Malpass, J. Randall Moorman
Abstract
Abstract Beaulieu-Jones and coworkers propose a litmus test for the field of predictive analytics—performance improvements must be demonstrated to be the result of non-clinician-initiated data, otherwise, there should be caution in assuming that predictive models could improve clinical decision-making (Beaulieu-Jones et al 2021). They demonstrate substantial prognostic information in unsorted physician orders made before the first midnight of hospital admission, and we are persuaded that it is fair to ask—if the physician thought of it first, what exactly is machine learning for in-patient risk stratification learning about? While we want predictive analytics to represent the leading indicators of a patient’s illness, does it instead merely reflect the lagging indicators of clinicians’ actions? We propose that continuous cardiorespiratory monitoring—‘routine telemetry data,’ in Beaulieu-Jones’ terms—represents the most valuable non-clinician-initiated predictive signal present in patient data, and the value added to patient care justifies the efforts and expense required. Here, we present a clinical and a physiological point of view to support our contention.