Applying machine learning on patient-reported data to model the selection of appropriate treatments for low back pain: A Pilot Study

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Abstract

The objective of this pilot study was to determine whether machine learning can be applied on patient-reported data to model decision-making on treatments for low back pain (LBP). We used a database of a university spine centre containing patient-reported data from 1546 patients with LBP. From this dataset, a training dataset with 354 features (input data) was labelled on treatments (output data) received by these patients. For this pilot study, we focused on two treatments: pain rehabilitation and surgery. Classification algorithms in WEKA were trained, and the resulting models were validated during 10-fold cross validation. Next to this, a test dataset was constructed - containing 50 cases judged on treatments by 4 master physician assistants (MPAs) - to test the models with data not used for training. We used prediction accuracy and average area under curve (AUC) as performance measures. The interrater agreement among the 4 MPAs was substantial (Fleiss Kappa 0.67). The AUC values indicated small to medium (machine) learning effects, meaning that machine learning on patient-reported data to model decision-making processes on treatments for LBP seems possible. However, model performances must be improved before these models can be used in real practice.
Original languageEnglish
Title of host publicationProceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020)
PublisherSCITEPRESS
Pages117-124
Number of pages8
Volume5: HEALTHINF
ISBN (Print)978-989-758-398-8
DOIs
Publication statusPublished - 24 Feb 2020
Event13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 - Valetta, Malta
Duration: 24 Feb 202026 Feb 2020
Conference number: 13
http://www.healthinf.biostec.org/

Conference

Conference13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020
Abbreviated titleBIOSTEC
CountryMalta
CityValetta
Period24/02/2026/02/20
Internet address

Keywords

  • Classification algorithms
  • Clinical decision support
  • Systems
  • Low back pain
  • Machine learning

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    Oude Nijeweme - d'Hollosy, W., van Velsen, L., Poel, M., Groothuis-Oudshoorn, C., Soer, R., Stegeman, P., & Hermens, H. (2020). Applying machine learning on patient-reported data to model the selection of appropriate treatments for low back pain: A Pilot Study. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) (Vol. 5: HEALTHINF, pp. 117-124). SCITEPRESS. https://doi.org/10.5220/0008962101170124