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Predictive monitoring using machine learning algorithms and a real‐life example on schizophrenia
Author(s) -
Huberts Leo C. E.,
Does Ronald J. M. M.,
Ravesteijn Bastian,
Lokkerbol Joran
Publication year - 2022
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2957
Subject(s) - machine learning , artificial intelligence , constant false alarm rate , computer science , alarm , mental health , schizophrenia (object oriented programming) , boosting (machine learning) , process (computing) , algorithm , set (abstract data type) , psychology , psychiatry , engineering , programming language , aerospace engineering , operating system
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.