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rdf:type |
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lifeskim:mentions |
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pubmed:issue |
6
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pubmed:dateCreated |
2010-7-27
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pubmed:abstractText |
Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent "pure" facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-10446819,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-11340738,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-12038648,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-12421743,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-15043240,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-16244013,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-18692963,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-19262917,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-19673146,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-8071797,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-843571,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-8722730,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-9120505,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20172803-9220806
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:month |
Jun
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pubmed:issn |
1558-2531
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
57
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
1457-66
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pubmed:dateRevised |
2011-7-28
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pubmed:meshHeading |
pubmed-meshheading:20172803-Algorithms,
pubmed-meshheading:20172803-Artificial Intelligence,
pubmed-meshheading:20172803-Humans,
pubmed-meshheading:20172803-Image Enhancement,
pubmed-meshheading:20172803-Image Interpretation, Computer-Assisted,
pubmed-meshheading:20172803-Infant, Newborn,
pubmed-meshheading:20172803-Pain,
pubmed-meshheading:20172803-Pain Measurement,
pubmed-meshheading:20172803-Pattern Recognition, Automated,
pubmed-meshheading:20172803-Reproducibility of Results,
pubmed-meshheading:20172803-Sensitivity and Specificity,
pubmed-meshheading:20172803-Signal Processing, Computer-Assisted,
pubmed-meshheading:20172803-Whole Body Imaging
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pubmed:year |
2010
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pubmed:articleTitle |
Relevance vector machine learning for neonate pain intensity assessment using digital imaging.
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pubmed:affiliation |
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA. behnood@gatech.edu
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, N.I.H., Extramural
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