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• W3100623206 endingPage "958" @default.
• W3100623206 startingPage "958" @default.
• W3100623206 abstract "In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer's disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions." @default.
• W3100623206 created "2020-11-23" @default.
• W3100623206 creator A5018421564 @default.
• W3100623206 creator A5058030251 @default.
• W3100623206 creator A5081103359 @default.
• W3100623206 date "2020-11-17" @default.
• W3100623206 modified "2023-10-14" @default.
• W3100623206 title "Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment" @default.
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• W3100623206 doi "https://doi.org/10.3390/diagnostics10110958" @default.
• W3100623206 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7697204" @default.
• W3100623206 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33212774" @default.
• W3100623206 hasPublicationYear "2020" @default.
• W3100623206 type Work @default.

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