Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients.

Objectives: This study describes the development and deployment of a machine learning (ML) model called Vent.io to predict mechanical ventilation (MV).

Materials And Methods: We trained Vent.

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.

Background: Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models.

Objective: The aim of this study was to optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model utility.

Methods: We calculated the excess costs of sepsis to the Centers for Medicare and Medicaid Services (CMS) by comparing patients with and without a secondary sepsis diagnosis but with the same primary diagnosis and baseline comorbidities.