Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

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dc.contributor.author Novaes Santana, Alex
dc.contributor.author Novaes de Santana, Charles
dc.contributor.author Montoya, Pedro
dc.date.accessioned 2021-02-01T09:20:35Z
dc.date.available 2021-02-01T09:20:35Z
dc.identifier.uri http://hdl.handle.net/11201/154912
dc.description.abstract [eng] 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.
dc.format application/pdf
dc.relation.isformatof https://doi.org/10.3390/diagnostics10110958
dc.relation.ispartof Diagnostics, 2020, vol. 10, num. 11, p. 1-18
dc.rights , 2020
dc.subject.classification 53 - Física
dc.subject.classification 159.9 - Psicologia
dc.subject.other 53 - Physics
dc.subject.other 159.9 - Psychology
dc.title Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment
dc.type info:eu-repo/semantics/article
dc.date.updated 2021-02-01T09:20:35Z
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.identifier.doi https://doi.org/10.3390/diagnostics10110958


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