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

Topics: Application of Health Informatics in Clinical Cases; Data Mining; e-Health; Human-Machine Interfaces for Disabled Persons; Medical Informatics; Pattern Recognition and Machine Learning; Physiological Modeling; Telemedicine; Wearable Health Informatics

Authors: Sara Khalid ; Andrew Judge and Rafael Pinedo-Villanueva

Affiliation: University of Oxford, United Kingdom

Keyword(s): Cluster Analysis, Unsupervised Learning, Electronic Healthcare Records, Chronic Pain, Data Mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Cloud Computing ; Data Mining ; Databases and Information Systems Integration ; Devices ; e-Health ; Enterprise Information Systems ; Health Information Systems ; Human-Computer Interaction ; Human-Machine Interfaces for Disabled Persons ; Pattern Recognition and Machine Learning ; Physiological Computing Systems ; Physiological Modeling ; Platforms and Applications ; Sensor Networks ; Signal Processing ; Soft Computing ; Telemedicine ; Wearable Sensors and Systems

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. (More)

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Paper citation in several formats:
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) - HEALTHINF; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, pages 266-273. DOI: 10.5220/0006535602660273

@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) - HEALTHINF},
year={2018},
pages={266-273},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006535602660273},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF
TI - An Unsupervised Learning Model for Pattern Recognition in Routinely Collected Healthcare Data
SN - 978-989-758-281-3
IS - 2184-4305
AU - Khalid, S.
AU - Judge, A.
AU - Pinedo-Villanueva, R.
PY - 2018
SP - 266
EP - 273
DO - 10.5220/0006535602660273
PB - SciTePress