<p>Predictive process monitoring aims to produce early warnings of unwanted events. We consider the use of the machine learning method extreme gradient boosting as the forecasting model in predictive monitoring. A tuning algorithm is proposed as the signaling method to produce a required false alarm rate. We demonstrate the procedure using a unique data set on mental health in the Netherlands. The goal of this application is to support healthcare workers in identifying the risk of a mental health crisis in people diagnosed with schizophrenia. The procedure we outline offers promising results and a novel approach to predictive monitoring.</p>

doi.org/10.1002/qre.2957, hdl.handle.net/1765/136310
Quality and Reliability Engineering International
Erasmus School of Economics

Leo C.E. Huberts, Ronald J.M.M. Does, B (Bastian) Ravesteijn, & Joran Lokkerbol. (2021). Predictive monitoring using machine learning algorithms and a real-life example on schizophrenia. Quality and Reliability Engineering International. doi:10.1002/qre.2957