# Machine Learning in Chronic Pain Research: A Scoping Review  
[PERSON]  
1 Department of Mathematics and Statistics, University of Tomsos--The Arctic University of Norway, NO-9019 Tomso, Norway; [EMAIL_ADDRESS] (M.D.K.J. ); [EMAIL_ADDRESS] (F.G.)  
[PERSON]  
2 Norwegian Centre for E-Health Research, P.O. Box 35, NO-9038 Tomso, Norway;
The most common definition of pain is \"an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage\" [14]. Chronic pain or persistent pain is pain that lasts more than three months (i.e., longer than the normal healing time of tissue). Chronic pain is different from acute pain. Acute pain is caused by a specific disease or injury. Acute pain is usually resolved as the injured tissue heals [15]. Chronic pain may arise from psychological states, does not serve any biological purpose, and does not have a predictable endpoint [16]. Despite recent advances in empirical pain research, effective methods for the treatment of chronic pain are still lacking. How individual patients with chronic pain respond to treatment and the adverse effects they experience are highly variable. In many cases, the cause of chronic pain is unknown, as there are no objective findings or measures that can be used to detect it. This makes treatment and management complex and time consuming. Due to the high variability among patients, successful management of chronic pain requires personalisation with respect to each patient's pain intensity and duration, disease state, tolerance of adverse events, and risk of medication abuse [17]. Identifying the underlying mechanisms of disease and enabling personalisation of care are key to improving the treatment of chronic pain. These are areas where machine learning (ML) is expected to be useful because ML excels at detecting patterns, rules, and causal dependencies in large datasets. ML algorithms learn from data without being explicitly programmed and can be used to obtain insights and make predictions and decisions from large and complex datasets. In healthcare, these analyses can help to better predict, identify, and treat diseases. To create reliable ML models, large volumes of relevant training data are needed. Increased adoption of information systems in healthcare creates vast amounts of health data and facilitates research in ML, which has the potential to realise person-centred healthcare. Simultaneously, citizens are increasingly using health apps and wearables for self-management, thus generating large amounts of health-related data outside of the formal healthcare sector. This wide range of health data, generated both in and out of clinical settings, can potentially be used by ML algorithms to support chronic pain research. This paper intends to determine the state-of-the-art knowledge in this field, identify directions for future studies using health data from numerous sources, and indicate which decision-support tools and recommendation systems for both patients and clinicians may be useful. Such tools can be built either by using existing chronic pain models or data-driven methods. ML is used both for classification (predicting discrete values such as male/female, pain/not chronic pain, etc.) and regression (predicting continuous values, such as height, salary, etc.). A ML algorithm can be supervised (labelled training data available) or unsupervised (no labelled training data available). Brief explanations of the most common ML algorithms follow below. For more information about ML algorithms, the book by [PERSON] [18] can be consulted. A naive Bayes classifier calculates the probability that a data point belongs to a certain class using the Bayes rule. The data point is assigned to the class to which it most likely belongs. This method is naive in the sense that it assumes the data points are independent of each other. Support vector machine (SVM) is a supervised method where the task is to create a hyperplane separating two classes.