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Authors: Wendy d’Hollosy 1 ; Lex van Velsen 2 ; Mannes Poel 3 ; Catharina Groothuis-Oudshoorn 4 ; Remko Soer 5 ; Patrick Stegeman 6 and Hermie Hermens 2

Affiliations: 1 University of Twente, EEMC/Biomedical Signals & Systems, Techmed, Personalised eHealth Technology, The Netherlands, Roessingh Research and Development, eHealth Cluster, Enschede, The Netherlands ; 2 Roessingh Research and Development, eHealth Cluster, Enschede, The Netherlands ; 3 University of Twente, EEMC/Data Science, The Netherlands ; 4 University of Twente, BMS/Health Technology and Services Research, Enschede, The Netherlands ; 5 University of Groningen, University Medical Center Groningen, Spine Center, Groningen, The Netherlands, Saxion University of Applied Science, Enschede, The Netherlands ; 6 University of Groningen, University Medical Center Groningen, Spine Center, Groningen, The Netherlands

ISBN: 978-989-758-398-8

ISSN: 2184-4305

Keyword(s): Classification Algorithms, Clinical Decision Support Systems, Low Back Pain, Machine Learning.

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 i ndicated 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. (More)

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Paper citation in several formats:
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 - HEALTHINF, ISBN 978-989-758-398-8 ISSN 2184-4305, pages 117-124. DOI: 10.5220/0008962101170124

@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 - HEALTHINF,},
year={2020},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008962101170124},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - 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
IS - 2184-4305
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

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