# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results
### Applications of Machine Learning in Chronic Pain Research
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Year** & **Condition** & **Data Size** & **Data Sources** & **ML Methods** \\ \hline Genomics approaches and biomarker identification & & & & & \\ \hline [PERSON] et al. [55] & 2018 & FM & 47 & Gene expression data & [PERSON]’s ratio \\ [PERSON] et al. [56] & 2018 & IC & 448 & Urine samples, questionnaires & Supervised learning \\ [PERSON] et al. [57] & 2016 & Chronic pain & 535 genes & Pain genes & ctional genomics analysis \\ [PERSON] et al. & 2016 & IC & 34 & DNA from stool and vaginal samples & extended random forest \\ \({}^{[58]}\) & & & & & \\ \hline Treatment & & & & & \\ \hline [PERSON] [59] & 2019 & CLBP & 62 & SEMG, VAS & RF, linear regression \\ [PERSON] [60] & 2019 & FM & 20 & (wrishstand sensors) & kNN, AdaBoost, SVM, RF, LDA, LUCCK \\ [PERSON] [61] & 2018 & CLBP & 1326 & Age, well-being, pain description & DT, RF, and boosted tree \\ [PERSON] [62] & 2018 & FM & 15,945 & EHR & Penalized regression \\ [PERSON] [63] & 2017 & CLBP & 78 & SEMG & SVM \\ [PERSON] et al. [64] & 2015 & CLBP & 22 & measurements, facial expressions, movement & SVM, RF \\ [PERSON] et al. [65] & 2014 & CLBP & 21 & Sit-to-stand and one-leg stand & RF \\ [PERSON] et al. [66] & 2013 & CLBP & 21 & The EmoPain dataset & RF \\ [PERSON] [25] & 2013 & Chronic neck & 794 & Patients records & DT, non-dominated sort \\  & pain & & & & \\ [PERSON] [67] & 2012 & CLBP & 53 & MRI & SVM \\ [PERSON] [68] & 2002 & CLBP & 9 & 3D vertebral motion & LDA and NN \\ \hline Self-management & & & & & \\ \hline [PERSON] et al. [69] & 2019 & Chronic pain & 144 & Pain Catastrophizing Scale, Pain Disability & Multilevel AR (1) modelling, SAS procedure & Scale, VAS & [70], \(\chi^{2}\)-test, logistic regression \\ [PERSON] [71] & 2018 & CLBP & 20 & MRI, PROM (pain-related fear) & MVPA, PRONTo [43] \\ [PERSON] [72] & 2018 & Chronic pain & 28,952 & Patient-generated data (mobile app: pain & Logistic regression, RF, SVM, k-means \\  & & & & data) & clustering \\ [PERSON] et al. [73] & 2018 & CLBP & 10 & Patient-generated data (mobile app: & \\  & & & & sensor-based and self-reported physical & RL \\ [PERSON] et al. [74] & 2011 & Chronic pain & 187 [75] & PROM & SVM \\ [PERSON] [76] & 2009 & Chronic pain & 187 [75] & PROM & DT, naive Bayes, SVM, MLP \\ \hline Measuring pain intensity & & & & & \\ \hline [PERSON] et al. [77] & 2019 & CLBP & 71 & Exacerbation manouvres, fMRI & SVM, SVR \\ [PERSON] et al. [78] & 2018 & Shoulder pain & 25 & Facial expression videos & CNN \\ [PERSON] and [PERSON] [79] & 2012 & Shoulder pain & 129 & Facial expression videos, PROM & SVM, active appearance model \\ [PERSON] et al. [80] & 2012 & Shoulder pain & 25 & Facial expression videos & Relevance vector regression, active appearance model \\ [PERSON] [81] & 2011 & Shoulder pain & 129 & Facial expression videos & SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)Efficient ML methods for classifying and clustering patients with chronic pain have proven to be useful in pain assessment and treatment decision-making. These methods have been used both for making a correct diagnosis and to identify subgroups among patients with the same diagnosis who potentially react differently to a certain treatment, such as two of the studies that aimed at identifying subgroups of FM patients by using clustering techniques [27; 31]. Examples of research more directly related to treatment include outcome prediction in acupuncture [25], suicide prediction among FM patients [62], and disc localisation in CLBP patients [67]. [PERSON] et al. [63] used SVM-based classifiers of surface electromyography (SEMG) data to identify low back pain (LBP) patients who would respond to a 12 week rehabilitation program. The best performing classifier achieved a classification accuracy of 97%. Thus, the authors claimed that their study could be helpful for physicians or therapists in recommending the best treatment for individual LBP patients.