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Improving pain management in patients with sickle cell disease from physiological measures using machine learning techniques
- Source :
- Smart Health. :48-59
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients’ pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.
- Subjects :
- Decision support system
business.industry
Medicine (miscellaneous)
Health Informatics
Disease
Pain management
Machine learning
computer.software_genre
Health informatics
Article
Computer Science Applications
03 medical and health sciences
0302 clinical medicine
Health Information Management
Pain assessment
Rating scale
Medicine
030212 general & internal medicine
Artificial intelligence
Stage (cooking)
business
computer
030217 neurology & neurosurgery
Information Systems
Multinomial logistic regression
Subjects
Details
- ISSN :
- 23526483
- Database :
- OpenAIRE
- Journal :
- Smart Health
- Accession number :
- edsair.doi.dedup.....bef0e324f97038bcfeda680e5e773a49