Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1302-1317 |
| Number of pages | 16 |
| Journal | Quality and Reliability Engineering International |
| Volume | 38 |
| Issue number | 3 |
| Early online date | 19 Jul 2021 |
| DOIs | |
| Publication status | Published - Apr 2022 |
Bibliographical note
Funding Information:The authors wish to thank the Statistics Netherlands Institute for giving access to the unique data set used in this study. Furthermore, the authors wish to thank the Trimbos Institute for providing mental healthcare practitioners' input on the current operation and the design of the monitoring system proposed in this article. The authors would also like to thank the editors and reviewers at Quality and Reliability Engineering International for their valuable comments on this paper.
Publisher Copyright:
© 2021 The Authors. Quality and Reliability Engineering International published by John Wiley & Sons Ltd
