Abstract
Due to digitalization, more and more data on an individual’s well-being is available in various repositories owned by different organizations. Intelligent data processing methods such as machine learning, enable efficient and accurate value creation from data. This paper addresses the problem of how to process big and mostly unstructured data to predict the work disability risk of an individual. Currently, the best data for predicting disability risk of an individual comes from health and employment records. However, no simple indicator can be reliably used to detect the risk. In our work, we present a ML model for assessing the risk of losing work ability based on anonymized medical reports of an occupational health care company. Our models are created using the ULMFit toolset and they reach accuracy of 72 % in a two class case and 65% in a three class case.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
OECD: Sickness, disability and work: breaking the barriers. OECD Publishing, Paris (2010). https://doi.org/10.1787/9789264088856-en
Rantala, J., Hietaniemi, M., Nyman, H., Laaksonen, M., Kuivalainen, S.: Työkyvyttömyyseläkkeensaajien eläketurva ja toimeentulo 2000-luvulla. Finnish Centre for Pensions, Helsinki (2017). ISBN 978-951-691-269-4
Finnish Centre for Pensions: Earnings-related pension recipients in Finland 2017. Helsinki: Finnish Centre for Pensions (2018). ISSN 2343-3558
Finnish Centre for Pensions: database. Available: https://tilastot.etk.fi/pxweb/fi/ETK/?rxid=a8cde648-be6c-4fb2-bf13667849df9069 Accessed 13 Jan. 2020
Pomaki, G., Franche, R., Murray, E., Khushrushahi, N., Lampinen, T.M.: Workplace-based work disability prevention interventions for workers with common mental health conditions: a review of the literature. Journal of Occupational Rehabilitation 22, 182-195 (2012). https://doi.org/10.1007/s10926011-9338-9
Airaksinen, J., et al.: Development and validation of a risk prediction model for work disability: multicohort study. Science Report 7(13578) (2017). https://doi.org/10.1038/s41598-017-13892-1
Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. In: Frontiers in Artificial Intelligence and Applications (2007). ISBN 978-1-58603-780-2
Zolbanin, H.M., Delen, D.: Processing electronic medical records to improve predictive analytics outcomes for hospital readmissions. Decis. Support Syst. 112, 98–110 (2018)
Ng, K., et al.: PARAMO: a Parallel predictive modeling platform for healthcare analytic research using electronic health records. Journal of Biomedical Informatics 48, 160-70 (2014). https://doi.org/10.1016/j.jbi.2013.12.012
Huhta-Koivisto, T.: Work disability risk prediction with machine learning. Master Thesis. Aalto University, School of Electrical Engineering. Espoo. p. 56 (2020)
Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1 (2018). https://doi.org/10.18653/v1/P18-1031
Shiri, R., et al.: A screening tool for the risk of disability retirement due to musculoskeletal disoders. Scandinavian Journal of Work, Enviroment & Health 44(1) (2018). https://doi.org/10.5271/sjweh.3684
van Amelsvoort, L., Beurskens, K., Schröer, C., Swaen, G.: Fatigue as a predictor of work disability. Occupational and Environmental Medicine 59(10) (2002). https://doi.org/10.1136/oem.59.10.712
Reho, T.: Frequent Attenders of Occupational Health Primary Care and Work Disability. Tampere University Dissertations 225 (2020). ISBN 978-952-03-1485-9
Pham, A., Névéol, A., Lavanrgne, T., et al.: Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings. BMC Bioinformatics 15 (2014)
Cardinal, R.N.: Clinical records anonymisation and text extraction (CRATE): an open-source software system. BMC Med. Inform. Decis. Mak. 17(1), 50 (2017)
Cunningham, H., Tablan, V., Roberts, A., Bontcheva, K.: Getting more out of biomedical documents with GATE’s full lifecycle open source text analytics. PLoS Comput. Biol. 9(2), e1002854 (2013)
Alamäki, A., Aunimo, L., Ketamo, H., Parvinen, L.: Interactive machine learning: managing information richness in highly anonymized conversation data. In: Working conference on virtual enterprises, pp. 173-184. Springer, Cham (2019)
Kauttonen, J., Aunimo, L.: Dialog Modelling Experiments with Finnish One-to-One Chat Data. In: Filchenkov, A., Kauttonen, J., Pivovarova, L. (eds.) AINL 2020. CCIS, vol. 1292, pp. 34–53. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59082-6_3
Chen, X., Wang, S., Fu, B., Long, M., Wang, J.: Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning. Adv. Neural. Inf. Process. Syst. 32, 1908–1918 (2019)
Devlin, J., Ming-Wei, C., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805v2 (2019)
Vaswani, A., et al.: Attention is All you Need. Advances in Neural Information Processing Systems, pp. 5998-6008 (2017)
Ding, M., Zhou, C., Yang, H., Tang, J.: CogLTX: Applying BERT to Long Texts. Advances in Neural Information Processing Systems 33 (2020)
Merity, S., Keskar, N.S., Socher, R.: Regularizing and optimizing LSTM language models. In: International Conference on Learning Representations (2017)
Artstein, R.: Inter-annotator agreement. In: Handbook of linguistic annotation, pp. 297–313. Springer, Dordrecht (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Huhta-Koivisto, V., Saarela, K., Nurminen, J.K. (2023). Work Disability Risk Prediction with Text Classification of Medical Reports. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). ACR 2023. Lecture Notes in Networks and Systems, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-031-33743-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-33743-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33742-0
Online ISBN: 978-3-031-33743-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)