PREDICTING THE EVOLUTION OF PRESSURE ULCERS

Francisco J. Veredas, Héctor Mesa, Juan C. Morilla, Laura Morente

Abstract

A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, treatment and care of pressure ulcers are costly for health services. Accurate wound evaluation is a critical task for optimizing the efficacy of treatment and care. Prediction of wound evolution helps the effective management of health resources and planning of pharmacological treatment and health-care decisions. In this paper, different machine learning approaches have been designed and used to predict the evolution of pressure ulcers. These predictive systems are based on local features extracted from wound images which were weekly taken in uncontrolled lighting conditions. The images were automatically segmented by the mean-shift procedure. A group of clinical experts manually classified the segmented regions into five different tissue types, and a set of local descriptors based on area measurements of these tissues was extracted. The one-week evolution of two different indicators for pressure ulcer evaluation is predicted: the ratio between granulation and devitalized tissue, and the percentage of wound-bed border consisting of granulation tissue. Of the tens of machine learning approaches and architectures tested in this study, support vector machines, naive Bayes classifiers, neural networks and decision trees achieved the highest accuracy rates in the prediction of the two indicators above, with also acceptable sensitivity and positive predictive value rates. Feature selection significantly reduced the number of input features needed for prediction. Neural networks and decision trees gave the best performance results, and the C4.5 algorithm achieved the highest accuracy rate (∼ 81%) in the prediction of the granulation/devitalized ratio from a small number of input features.

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Paper Citation


in Harvard Style

J. Veredas F., Mesa H., C. Morilla J. and Morente L. (2010). PREDICTING THE EVOLUTION OF PRESSURE ULCERS . In Proceedings of the Third International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2010) ISBN 978-989-674-016-0, pages 5-12. DOI: 10.5220/0002690700050012


in Bibtex Style

@conference{healthinf10,
author={Francisco J. Veredas and Héctor Mesa and Juan C. Morilla and Laura Morente},
title={PREDICTING THE EVOLUTION OF PRESSURE ULCERS},
booktitle={Proceedings of the Third International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2010)},
year={2010},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002690700050012},
isbn={978-989-674-016-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2010)
TI - PREDICTING THE EVOLUTION OF PRESSURE ULCERS
SN - 978-989-674-016-0
AU - J. Veredas F.
AU - Mesa H.
AU - C. Morilla J.
AU - Morente L.
PY - 2010
SP - 5
EP - 12
DO - 10.5220/0002690700050012