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
› Author Affiliations


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.

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. (

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

  • References

  • 1 Rafter N, Hickey A, Condell S. et al. Adverse events in healthcare: learning from mistakes. QJM 2015; 108 (04) 273-277
  • 2 Slawomirski L, Auraaen A, Klazinga N. The economics of patient safety: strengthening a value-based approach to reducing patient harm at national level. Paris: OECD; 2015. Accessed February 22, 2022 at:
  • 3 Jonsson PM, Øvretveit J. Patient claims and complaints data for improving patient safety. Int J Health Care Qual Assur 2008; 21 (01) 60-74
  • 4 Järvelin J, Häkkinen U, Rosenqvist G, Remes V. Factors predisposing to claims and compensations for patient injuries following total hip and knee arthroplasty. Acta Orthop 2012; 83 (02) 190-196
  • 5 Vallila N, Sommarhem A, Paavola M, Nietosvaara Y. Pediatric distal humeral fractures and complications of treatment in Finland: a review of compensation claims from 1990 through 2010. J Bone Joint Surg Am 2015; 97 (06) 494-499
  • 6 Nokso-Koivisto J, Blomgren K, Aaltonen LM, Lehtonen L, Helmiö P. Patient injuries in pediatric otorhinolaryngology. Int J Pediatr Otorhinolaryngol 2019; 120: 36-39
  • 7 Swanljung O, Vehkalahti MM. Root canal irrigants and medicaments in endodontic malpractice cases: a nationwide longitudinal observation. J Endod 2018; 44 (04) 559-564
  • 8 Vehkalahti MM, Swanljung O. Trends in endodontic malpractice claims and their indemnity in Finland in the 2000s. J Dentistry & Oral Health 2017; 4: 103
  • 9 Vintturi J, Niemi J, Welling M, Kampman O. Psykiatristen potilasvahinkojen yleisyys ja luokittelu (in Finnish). Duodecim 2022; 138: 84-90
  • 10 Gómez-Durán EL, Martin-Fumadó C, Benet-Travé J, Arimany-Manso J. Malpractice risk at the physician level: claim-prone physicians. J Forensic Leg Med 2018; 58: 152-154
  • 11 Jena AB, Seabury S, Lakdawalla D, Chandra A. Malpractice risk according to physician specialty. N Engl J Med 2011; 365 (07) 629-636
  • 12 Martin-Fumadó C, Gómez-Durán EL, Rodríguez-Pazos M, Arimany-Manso J. Medical professional liability in psychiatry. Actas Esp Psiquiatr 2015; 43 (06) 205-212
  • 13 Chekroud AM, Zotti RJ, Shehzad Z. et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 2016; 3 (03) 243-250
  • 14 Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds III CF, Aizenstein HJ. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry 2015; 30 (10) 1056-1067
  • 15 Schmaal L, Marquand AF, Rhebergen D. et al. Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study. Biol Psychiatry 2015; 78 (04) 278-286
  • 16 Lin E, Lin CH, Lai YL, Huang CH, Huang YJ, Lane HY. Combination of G72 genetic variation and G72 protein level to detect schizophrenia: machine learning approaches. Front Psychiatry 2018; 9: 566
  • 17 Bishop CM. Pattern Recognition and Machine Learning. New Delhi: Springer Science + Business Media; 2000
  • 18 Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA. Data Mining, A Knowledge Discovery Approach. New York, NY: Springer Science + Business Media; 2007
  • 19 Flach P. Machine Learning, The Art and Science of Algorithms that Make Sense of Data. New York, NY: Cambridge University Press; 2012
  • 20 Breiman L. Random Forests. Dordrecht: Kluwer Academic Publishers; 2000
  • 21 Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002; 16: 321-357
  • 22 Chandola V, Sukumar SR, Schryver J. Knowledge discovery from massive healthcare claims data. Paper presented at: Proceedings of the 19th ACM SIGKDD International Conference Knowledge Discovery and Data Mining; August 11, 2013–Aug 14, 2013, Chicago, United States; 2013: 1312-1320
  • 23 Bonetti M, Cirillo P, Musile Tanzi P, Trinchero E. An analysis of the number of medical malpractice claims and their amounts. PLoS One 2016; 11 (04) e0153362
  • 24 Li H, Wu X, Sun T. et al. Claims, liabilities, injures and compensation payments of medical malpractice litigation cases in China from 1998 to 2011. BMC Health Serv Res 2014; 14: 390
  • 25 Baidwan NK, Carroll NW, Ozaydin B, Puro N. Analyzing workers' compensation claims and payments made using data from a large insurance provider. Int J Environ Res Public Health 2020; 17 (19) 7157
  • 26 Prang KH, Hassani-Mahmooei B, Collie A. Compensation research database: population-based injury data for surveillance, linkage and mining. BMC Res Notes 2016; 9 (01) 456
  • 27 Large MM. Relationship between compensation claims for psychiatric injury and severity of physical injuries from motor vehicle accidents. Med J Aust 2001; 175 (03) 129-132
  • 28 Nilsson L, Borgstedt-Risberg M, Brunner C. et al. Adverse events in psychiatry: a national cohort study in Sweden with a unique psychiatric trigger tool. BMC Psychiatry 2020; 20 (01) 44
  • 29 Marcus SC, Hermann RC, Frankel MR, Cullen SW. Safety of psychiatric inpatients at the veterans health administration. Psychiatr Serv 2018; 69 (02) 204-210