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

Wendy d’Hollosy, Lex van Velsen, Mannes Poel, Catharina Groothuis-Oudshoorn, Remko Soer, Patrick Stegeman, Hermie Hermens

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.

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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 - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 117-124. DOI: 10.5220/0008962101170124


in Bibtex Style

@conference{healthinf20,
author={Wendy d’Hollosy and Lex van Velsen and Mannes Poel and Catharina 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 - 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 - 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