Applying Machine Learning on Patient-Reported Data to Model the Selection of Appropriate Treatments for Low Back Pain: A Pilot Study

Wendy Oude Nijeweme – d’Hollosy, Wendy Oude Nijeweme – d’Hollosy, Lex van Velsen, Mannes Poel, Catharina G. M. Groothuis-Oudshoorn, Remko Soer, Remko Soer, Patrick Stegeman, Hermie Hermens

2020

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.

Paper Citation

in Harvard Style

d’Hollosy W., van Velsen L., Poel M., Groothuis-Oudshoorn C., Soer R., Stegeman P. and 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) - Volume 5: HEALTHINF; ISBN 978-989-758-398-8, SciTePress, pages 117-124. DOI: 10.5220/0008962101170124

in Bibtex Style

@conference{healthinf20,
author={Wendy Oude Nijeweme – d’Hollosy and Lex van Velsen and Mannes Poel and Catharina G. M. Groothuis-Oudshoorn and Remko Soer and Patrick Stegeman and Hermie Hermens},
title={Applying Machine Learning on Patient-Reported Data to Model the Selection of Appropriate Treatments for Low Back Pain: A Pilot Study},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF},
year={2020},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008962101170124},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF
TI - Applying Machine Learning on Patient-Reported Data to Model the Selection of Appropriate Treatments for Low Back Pain: A Pilot Study
SN - 978-989-758-398-8
AU - d’Hollosy W.
AU - van Velsen L.
AU - Poel M.
AU - Groothuis-Oudshoorn C.
AU - Soer R.
AU - Stegeman P.
AU - Hermens H.
PY - 2020
SP - 117
EP - 124
DO - 10.5220/0008962101170124
PB - SciTePress