An Unsupervised Learning Model for Pattern Recognition in Routinely Collected Healthcare Data

Sara Khalid, Andrew Judge, Rafael Pinedo-Villanueva

2018

Abstract

This study examines a large routinely collected healthcare database containing patient-level self-reported outcomes following knee replacement surgery. A model based on unsupervised machine learning methods, including k-means and hierarchical clustering, is proposed to detect patterns of pain experienced by patients and to derive subgroups of patients with different outcomes based on their pain characteristics. Results showed the presence of between two and four different sub-groups of patients based on their pain characteristics. Challenges associated with unsupervised learning using real-world data are described and an approach for evaluating models in the presence of unlabelled data using internal and external cluster evaluation techniques is presented, that can be extended to other unsupervised learning applications within healthcare and beyond. To our knowledge, this is the first study proposing an unsupervised learning model for characterising pain-based patient subgroups using the UK NHS PROMs database.

Paper Citation

in Harvard Style

Khalid S., Judge A. and Pinedo-Villanueva R. (2018). An Unsupervised Learning Model for Pattern Recognition in Routinely Collected Healthcare Data. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF; ISBN 978-989-758-281-3, SciTePress, pages 266-273. DOI: 10.5220/0006535602660273

in Bibtex Style

@conference{healthinf18,
author={Sara Khalid and Andrew Judge and Rafael Pinedo-Villanueva},
title={An Unsupervised Learning Model for Pattern Recognition in Routinely Collected Healthcare Data},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF},
year={2018},
pages={266-273},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006535602660273},
isbn={978-989-758-281-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF
TI - An Unsupervised Learning Model for Pattern Recognition in Routinely Collected Healthcare Data
SN - 978-989-758-281-3
AU - Khalid S.
AU - Judge A.
AU - Pinedo-Villanueva R.
PY - 2018
SP - 266
EP - 273
DO - 10.5220/0006535602660273
PB - SciTePress