Predictive monitoring using machine learning algorithms and a real-life example on schizophrenia

Leo C.E. Huberts*, Ronald J.M.M. Does, Bastian Ravesteijn, Joran Lokkerbol

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
35 Downloads (Pure)

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 languageEnglish
Pages (from-to)1302-1317
Number of pages16
JournalQuality and Reliability Engineering International
Volume38
Issue number3
Early online date19 Jul 2021
DOIs
Publication statusPublished - 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

Fingerprint

Dive into the research topics of 'Predictive monitoring using machine learning algorithms and a real-life example on schizophrenia'. Together they form a unique fingerprint.

Cite this