Human Studies

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.

Fibromyalgia syndrome (FM) is a complex disease that is mainly characterised by chronic widespread pain, fatigue and sleep disturbances and may be precipitated or worsened by many stressors. The aim of this study was to observe the behaviour of FM symptoms during the course of coronavirus disease 2019 (COVID-19).

This is a prospective, blinded, case-control study of patients with chronic pain using body diagrams and colored markers to show the distribution and quality of pain and sensory symptoms (aching, burning, tingling, numbness, and sensitivity to touch) experienced in affected body parts.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic pain condition that significantly affects patient quality of life. We investigated whether receiving a formal medical diagnosis of IC/BPS was perceived by patients to improve symptoms and disease-specific quality of life.