Title |
Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning
|
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Published in |
BMC Research Notes, January 2018
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DOI | 10.1186/s13104-018-3194-z |
Pubmed ID | |
Authors |
Anne Estrup Olesen, Debbie Grønlund, Mikkel Gram, Frank Skorpen, Asbjørn Mohr Drewes, Pål Klepstad |
Abstract |
Use of opioids for pain management has increased over the past decade; however, inadequate analgesic response is common. Genetic variability may be related to opioid efficacy, but due to the many possible combinations and variables, statistical computations may be difficult. This study investigated whether data processing with support vector machine learning could predict required opioid dose in cancer pain patients, using genetic profiling. Eighteen single nucleotide polymorphisms (SNPs) within the µ and δ opioid receptor genes and the catechol-O-methyltransferase gene were selected for analysis. Data from 1237 cancer pain patients were included in the analysis. Support vector machine learning did not find any associations between the assessed SNPs and opioid dose in cancer pain patients, and hence, did not provide additional information regarding prediction of required opioid dose using genetic profiling. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 48 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 7 | 15% |
Researcher | 5 | 10% |
Student > Ph. D. Student | 4 | 8% |
Student > Bachelor | 3 | 6% |
Other | 3 | 6% |
Other | 9 | 19% |
Unknown | 17 | 35% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 16 | 33% |
Biochemistry, Genetics and Molecular Biology | 4 | 8% |
Nursing and Health Professions | 2 | 4% |
Computer Science | 2 | 4% |
Psychology | 1 | 2% |
Other | 3 | 6% |
Unknown | 20 | 42% |