Forecasting algorithms in the ICU

Abstract

Despite significant advances in modeling methods and access to large datasets, there are very few real-time forecasting systems deployed in highly monitored environment such as the intensive care unit. Forecasting models may be developed as classification, regression or time-to-event tasks; each could be using a variety of machine learning algorithms. An accurate and useful forecasting systems include several components beyond a forecasting model, and its performance is assessed using end-user-centered metrics. Several barriers to implementation and acceptance persist and clinicians will play an active role in the successful deployment of this promising technology.

Keywords: Alarming systems; Artificial intelligence; Early warning systems; Forecasting instability; Predictive analytics.