# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results
### Data  
The amount of health-related data collected and stored is increasing at a rapid pace. Due in particular to advances in medical imaging and genomics, substantial volumes of health data are stored in clinical systems. In addition, the increased availability and use of consumer health technologies, such as smartphones, mobile apps, and wearable devices, have led to the availability of patient-generated health data. Hence, a wide range of health data exists, both in and out of clinical settings, which can potentially be used by ML algorithms in chronic pain research.  
Health data are either structured or unstructured. Structured data are easier to store and access and can be used in the development of ML algorithms. Examples of structured data that have been used for chronic pain diagnosis and treatment include electronic health record (EHR) data [25; 32; 62], claims data [27], International Classification of Diseases (ICD) codes, and patient-generated data (from questionnaires or mobile phone applications) [26; 56; 72]. Feature selection in structured data can be systematically done with the help of statistical analysis, similar analysis studies, and guidelines [27].  
Unstructured data are data that do not have a data model or are not organised in a predefined manner. The most common unstructured data in the studies included in this review are magnetic resonance imaging (MRI) data. MRI involves the use of strong magnetic fields and radio waves to generate images of the organs in the body. Both fMRI and structural magnetic resonance imaging (sMRI) images are widely used in classifying patients with chronic pain versus healthy controls. Other imaging data include facial expressions used to measure pain intensity [78; 79; 80; 81]. Medical texts are important parts of unstructured data in the EHRs that can provide useful information. Natural language processing has been used to obtain pain-related information from medical texts [39; 50]. Sensor data such as gait information and electromyography (EMG) are also useful in examining the kinematic and kinetic parameters of the gait of patients [35; 36] or measuring muscle activity.  
Patient-generated data are also emerging as important for chronic pain research. Wearable devices such as wristbands may provide biometric and physical activity data [60]. Sensor data and patient self-reported data can complement EHR data to provide a more comprehensive picture of a patient's health status. Questionnaires or self-reported data can be either structured or unstructured, depending on whether the data are in the form of multiple choice responses or free-text, such as patients who can indicate the frequency of pain symptoms they experienced on a point scale. ML methods that are used to analyse structured questionnaires data include adaptive networks [26].  
The availability of training data is often an obstacle for ML research in health settings, due to strict privacy and confidentiality regulations. Hence, most of the datasets used in the included studies are not publicly available. However, a few are available for academic use, and these are shown in Table 2.