# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results
### Applications of Machine Learning in Chronic Pain Research
In another study [61], a clinical decision support tool was built for treatment of LBP using three ML models (DT, RF, and boosted tree). Self-management has also been shown to be a key application of machine learning in empowering the patients to manage their condition. Six of the included papers focused on self-management of chronic pain [69; 71; 72; 73; 74; 76]. Two of these were authored by the same group of researchers, with both studies using the same training dataset [74; 76]. They aimed to support patient self-management by analysing self-reported data from patients with chronic pain pre-treatment, post-treatment, and at three month follow-up. The main topics of the studies were prediction of patients' health level [76] and feature selection to optimise questionnaires by omitting irrelevant questions [74]. Three studies were related to fear-avoidance behaviour and anticipation of pain [69; 71; 73]. Regular physical activity is an important factor for successful management of chronic musculoskeletal pain [73]. However, many patients avoid physical activity due to a strong fear of pain. [PERSON] et al. [69] aimed to identify individuals who are prone to catastrophise their pain, by analysing daily assessment data from a mobile application for patients with chronic pain. [PERSON] et al. [71] analysed self-reported questionnaire data and functional magnetic resonance imaging (fMRI) of patients with CLBP to estimate the level of pain-related fear in individuals. [PERSON] et al. [73] examined the feasibility of a mobile app to promote physical activity among patients suffering from chronic back pain. The application uses ML to analyse sensor data and self-reported physical activity data to generate personalised physical activity recommendations based on the patient's behaviour. One study focused on measuring and predicting pain volatility by employing ML methods to analyse data from users of a pain management app [72]. Valid and reliable assessment of pain is essential in diagnostics of chronic pain and in guiding treatment decisions [82]. This review includes five studies focusing on automatic estimation of pain intensity. Of these, four studies used facial expression videos from a publicly available shoulder pain research database as the training data [78; 79; 80; 81]. The training data were annotated, and all four studies used algorithms for supervised learning. In two of the studies, a SVM was applied to classify discrete levels of pain intensity [79; 81]. [PERSON] et al. [80] applied the relevance vector regression algorithm for continuous estimation of pain intensity. [PERSON] et al. [78] used a convolutional neural network (CNN) to estimate pain intensity by regression. [PERSON] et al. [77] used ML to predict clinical pain based on brain imaging data (fMRI) and physiological data from patients with CLBP. This study used support vector regression (SVR) for the prediction of pain intensity on a continuous scale. In research settings, brain imaging approaches can help to better understand the underlying mechanisms of pain.