Classification of health level from chronic pain self reporting

Yan Huang, Huiru Zheng, Christopher Nugent, P. J. McCullagh, Norman Black, Kevin Vowles, Lance McCracken

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper proposes an approach to identify patients' health levels based on the information gathered following a process of self reporting based on the patient's current condition. The goal of approach is the accurate provision of information to assist with self management of chronic pain. Four supervised classifiers, namely decision tree, naive Bayes, support vector machine and multilayer perceptron, have been applied to classify the health level of patients suffering from chronic pain based on information collected from self reports from three treatment stages - pre-treatment stage, post-treatment stage and 3-month follow-up stage. Three binary classification problems, i.e. pre-treatment vs. post-treatment, pre-treatment vs. 3-month follow-up and post-treatment vs. 3-month follow-up, were investigated. The classification accuracy and area under Receiver Operating Characteristics (ROC) curve ranged from 66.7% 94.7% and 0.689 0.989 respectively. The multilayer perceptron classifier achieved the best performance with a classification accuracy of 94.7% and area under ROC curve of 0.981 for the pre-treatment vs. post-treatment classification. The results from this study have demonstrated that it is feasible to apply automated classification techniques to identify patients' health level from their self reports. This data may be used as an important indicator in automated approaches to chronic disease self management, an area which is currently receiving much attention. Further work will investigate the presence of optimal features derived from questionnaires to improve the classification performance.

Conference

Conferencethe IADIS International Conf Proceedings of the IADIS International Conference e-Health 2009, Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009
Period1/06/09 → …

Fingerprint

Health
Multilayer neural networks
Classifiers
Decision trees
Support vector machines

Cite this

Huang, Y., Zheng, H., Nugent, C., McCullagh, P. J., Black, N., Vowles, K., & McCracken, L. (2009). Classification of health level from chronic pain self reporting. In Unknown Host Publication (pp. 43-50)
Huang, Yan ; Zheng, Huiru ; Nugent, Christopher ; McCullagh, P. J. ; Black, Norman ; Vowles, Kevin ; McCracken, Lance. / Classification of health level from chronic pain self reporting. Unknown Host Publication. 2009. pp. 43-50
@inproceedings{5c44ded4398c4b2d95f1e732e881677e,
title = "Classification of health level from chronic pain self reporting",
abstract = "This paper proposes an approach to identify patients' health levels based on the information gathered following a process of self reporting based on the patient's current condition. The goal of approach is the accurate provision of information to assist with self management of chronic pain. Four supervised classifiers, namely decision tree, naive Bayes, support vector machine and multilayer perceptron, have been applied to classify the health level of patients suffering from chronic pain based on information collected from self reports from three treatment stages - pre-treatment stage, post-treatment stage and 3-month follow-up stage. Three binary classification problems, i.e. pre-treatment vs. post-treatment, pre-treatment vs. 3-month follow-up and post-treatment vs. 3-month follow-up, were investigated. The classification accuracy and area under Receiver Operating Characteristics (ROC) curve ranged from 66.7{\%} 94.7{\%} and 0.689 0.989 respectively. The multilayer perceptron classifier achieved the best performance with a classification accuracy of 94.7{\%} and area under ROC curve of 0.981 for the pre-treatment vs. post-treatment classification. The results from this study have demonstrated that it is feasible to apply automated classification techniques to identify patients' health level from their self reports. This data may be used as an important indicator in automated approaches to chronic disease self management, an area which is currently receiving much attention. Further work will investigate the presence of optimal features derived from questionnaires to improve the classification performance.",
author = "Yan Huang and Huiru Zheng and Christopher Nugent and McCullagh, {P. J.} and Norman Black and Kevin Vowles and Lance McCracken",
year = "2009",
month = "6",
language = "English",
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Huang, Y, Zheng, H, Nugent, C, McCullagh, PJ, Black, N, Vowles, K & McCracken, L 2009, Classification of health level from chronic pain self reporting. in Unknown Host Publication. pp. 43-50, the IADIS International Conf Proceedings of the IADIS International Conference e-Health 2009, Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009, 1/06/09.

Classification of health level from chronic pain self reporting. / Huang, Yan; Zheng, Huiru; Nugent, Christopher; McCullagh, P. J.; Black, Norman; Vowles, Kevin; McCracken, Lance.

Unknown Host Publication. 2009. p. 43-50.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Classification of health level from chronic pain self reporting

AU - Huang, Yan

AU - Zheng, Huiru

AU - Nugent, Christopher

AU - McCullagh, P. J.

AU - Black, Norman

AU - Vowles, Kevin

AU - McCracken, Lance

PY - 2009/6

Y1 - 2009/6

N2 - This paper proposes an approach to identify patients' health levels based on the information gathered following a process of self reporting based on the patient's current condition. The goal of approach is the accurate provision of information to assist with self management of chronic pain. Four supervised classifiers, namely decision tree, naive Bayes, support vector machine and multilayer perceptron, have been applied to classify the health level of patients suffering from chronic pain based on information collected from self reports from three treatment stages - pre-treatment stage, post-treatment stage and 3-month follow-up stage. Three binary classification problems, i.e. pre-treatment vs. post-treatment, pre-treatment vs. 3-month follow-up and post-treatment vs. 3-month follow-up, were investigated. The classification accuracy and area under Receiver Operating Characteristics (ROC) curve ranged from 66.7% 94.7% and 0.689 0.989 respectively. The multilayer perceptron classifier achieved the best performance with a classification accuracy of 94.7% and area under ROC curve of 0.981 for the pre-treatment vs. post-treatment classification. The results from this study have demonstrated that it is feasible to apply automated classification techniques to identify patients' health level from their self reports. This data may be used as an important indicator in automated approaches to chronic disease self management, an area which is currently receiving much attention. Further work will investigate the presence of optimal features derived from questionnaires to improve the classification performance.

AB - This paper proposes an approach to identify patients' health levels based on the information gathered following a process of self reporting based on the patient's current condition. The goal of approach is the accurate provision of information to assist with self management of chronic pain. Four supervised classifiers, namely decision tree, naive Bayes, support vector machine and multilayer perceptron, have been applied to classify the health level of patients suffering from chronic pain based on information collected from self reports from three treatment stages - pre-treatment stage, post-treatment stage and 3-month follow-up stage. Three binary classification problems, i.e. pre-treatment vs. post-treatment, pre-treatment vs. 3-month follow-up and post-treatment vs. 3-month follow-up, were investigated. The classification accuracy and area under Receiver Operating Characteristics (ROC) curve ranged from 66.7% 94.7% and 0.689 0.989 respectively. The multilayer perceptron classifier achieved the best performance with a classification accuracy of 94.7% and area under ROC curve of 0.981 for the pre-treatment vs. post-treatment classification. The results from this study have demonstrated that it is feasible to apply automated classification techniques to identify patients' health level from their self reports. This data may be used as an important indicator in automated approaches to chronic disease self management, an area which is currently receiving much attention. Further work will investigate the presence of optimal features derived from questionnaires to improve the classification performance.

M3 - Conference contribution

SN - 9789728924812

SP - 43

EP - 50

BT - Unknown Host Publication

ER -

Huang Y, Zheng H, Nugent C, McCullagh PJ, Black N, Vowles K et al. Classification of health level from chronic pain self reporting. In Unknown Host Publication. 2009. p. 43-50