# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results  
### Applications of Machine Learning in Chronic Pain Research  
The results of the review are summarised in Table 1. In this table, the publications are grouped into six different categories with respect to the objectives of the studies and which stages of chronic pain care were addressed (RQ5):  
* Classification/diagnosis of patients with chronic pain using structured health data
* Classification/diagnosis of patients with chronic pain using text and images
* Genomics approaches and pain biomarker identification
* Treatment
* Self-management
* Measurement of pain intensity  
For each publication, information was extracted and is represented as columns in Table 1 for answering the research questions (RQ1-RQ5) as follows: chronic pain condition (RQ4), data size and data sources (RQ2 and RQ3), and ML methods (RQ1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Year** & **Condition** & **Data Size** & **Data Sources** & **ML Methods** \\ \hline Classifying patients & & & & & \\ using structured health data & & & & & \\ \hline [PERSON] [26] & 2018 & IBS, FM, CFS & 1751 & Symptom frequency questionnaire & Adaptive network model, k-means \\  & & & & & & clustering, NN \\ [PERSON] et al. [27] & 2018 & FM & 2529 & De-identified clinic-level data & K-means clustering, principal component \\ [PERSON] et al. [28] & 2018 & MPS & 56 & SEMG & Analysis, discriminant analysis \\  & & & & & \\  & & & & & \\ [PERSON] and [PERSON] [29] & 2017 & CPP & 346 & the full history of the patient. Seven different & Naive Bayes, C4, SVM, AdaBoost, kNN, \\  & & & & & conditions related to pelvic pain were & Weka software [50] \\ [PERSON] et al. [31] & 2017 & FM & 313 & Demographic characteristics & Clustering \\ [PERSON] et al. [32] & 2015 & FM & 587,960 & Structured EHR data (Humelica database) & RF \\ [PERSON] et al. [33] & 2015 & FM & 127 & Psychopathological and medical-social & AdaBoost \\  & & & & & features, VAS & \\ [PERSON] et al. [34] & 2014 & CLBP & 31 & Movements, muscle activity characteristics, & One-way ANOVA, SVM \\ [PERSON] [35] & 2013 & CLBP & 40 & Gait data & kStar, SVM, MLP, DT \\ [PERSON] et al. [36] & 2012 & CRPS & 20 & Gait data & MLP, SVM, RF, LDA, KStar \\ [PERSON] et al. [37] & 2006 & CFS & 103 & Clinical data & Nonlinear regression \\ \hline Classifying patients using text and images & & & & & \\ \hline [PERSON] [38] & 2018 & Musculoskeletal pain & 92 & Clinical notes from EHR & kNN, DT, SVM, RF \\ [PERSON] [59] & 2018 & CLBP & 871 & Lumbar spine imaging, radiology reports & Natural language processing \\ [PERSON] [40] & 2018 & TN & 46 & MRI & SVM \\ [PERSON] [41] & 2018 & AS & 133 & Questionnaire, fMRI & Multiple linear regression \\ [PERSON] et al. [42] & 2018 & AS & 133 & fMRI & PRONTo [43] \\ [PERSON] [44] & 2018 & CLBP & 97 & fMRI (+test data from OpenPain project) & SVM, conditional variational autoencoder \\ [PERSON] [45] & 2018 & CFS & 33 & fMRI and PROM & SVM \\ [PERSON] [46] & 2017 & FM & 72 & fMRI & SVM \\ [PERSON] et al. [47] & 2016 & CLBP & 22 & Body movement and EMG (EmoPain dataset) & RF, kNN, hidden Markov model \\  & & & & & Motion capture, EMG, video, audio, & \\ [PERSON] et al. [48] & 2015 & CLBP & 53 & and Hospital Anxiety and Depression Scale & SVM, RF \\  & & & & (EmoPain dataset) & \\ [PERSON] [49] & 2015 & IBS & 212 & MRI & MVPA \\ [PERSON] et al. [50] & 2015 & FM & 26 & sMRI,PROOM (mood and pain intensity) & Naive Bayes, logistic regression, kNN, MLP, \\ [PERSON] [51] & 2014 & FM, RA & 50 & fMRI, VAS & MVPA, SVM, DT, RF; naive Bayes, LDA, MLP \\ [PERSON] et al. [52] & 2014 & CLBP & 26 & fMRI & Sparse logistic regression \\ [PERSON] [53] & 2014 & CPP & 66 & sMRI & SVM \\ [PERSON] [54] & 2012 & CLBP & 94 & sMRI & SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Decisions: Classifying using health data, classifying using text and images, genomics approaches and biomarker identification, treatment, self-management, and measuring pain intensity.