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CC BY-NC-ND 4.0 · Methods Inf Med 2023; 62(05/06): 174-182
DOI: 10.1055/s-0043-1771378
Original Article

Machine Learning Classification of Psychiatric Data Associated with Compensation Claims for Patient Injuries

Martti Juhola
1   Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
,
Tommi Nikkanen
1   Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
,
Juho Niemi
2   Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
,
Maiju Welling
3   Patient Insurance Centre, Helsinki, Finland
,
Olli Kampman
2   Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
4   Department of Psychiatry, Tampere University Hospital, Pirkanmaa Hospital District, Tampere, Finland
5   Department of Clinical Sciences (Psychiatry), Umeå University, Umeå, Sweden and Västerbotten Welfare Region, Umeå, Sweden
6   Department of Clinical Sciences (Psychiatry), University of Turku, Turku, Finland
7   The Wellbeing Services County of Ostrobothnia, Department of Psychiatry, Vaasa, Finland
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Abstract

Background Adverse events are common in health care. In psychiatric treatment, compensation claims for patient injuries appear to be less common than in other medical specialties. The most common types of patient injury claims in psychiatry include diagnostic flaws, unprevented suicide, or coercive treatment deemed as unnecessary or harmful.

Objectives The objective was to study whether it is possible to form different categories of patient injury types associated with the psychiatric evaluations of compensation claims and to base machine learning classification on these categories. Further, the binary classification of positive and negative decisions for compensation claims was the other objective.

Methods Finnish psychiatric specialist evaluations for the compensation claims of patient injuries were classified into six different categories called classes applying the machine learning methods of artificial intelligence. In addition, another classification of the same data into two classes was performed to test whether it was possible to classify data cases according to their known decisions, either accepted or declined compensation claim.

Results The former classification task produced relatively good classification results subject to separating between different classes. Instead, the latter was more complex. However, classification accuracies of both tasks could be improved by using the generation of artificial data cases in the preprocessing phase before classifications. This preprocessing improved the classification accuracy of six classes up to 88% when the method of random forests was used for classification and that of the binary classification to 89%.

Conclusion The results show that the objectives defined were possible to solve reasonably.

Keywords

machine learning - classification - psychiatry - patient injury


Publication History

Received: 08 April 2022

Accepted: 25 May 2023

Article published online:
24 July 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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