# Machine Learning in Chronic Pain Research: A Scoping Review
## 4 Discussion
### Future Research Directions  
As noted previously, few of the included studies focused on the use of ML in the treatment of chronic pain. This observation, combined with the fact that learning from data can facilitate the treatment of patients with chronic pain, indicates that decision support tools and recommendation systems for both patients and clinicians may be useful. Tools can be built either by using existing chronic pain models or based on data-driven methods.  
At present, no satisfactory treatment exists for patients with chronic pain. There are at least two important reasons for this: First, chronic pain is not a life-threatening disease. Hence, it attracts less attention and is considered to be less of a priority in healthcare service [100]. Second, the population of patients with chronic pain is large and heterogeneous. Identifying the best treatment for an individual patient is challenging. ML may be key to remedy this problem. The underlying concept is that patients and clinicians can learn about the best treatment in a specific case based on real-world patient data. Existing analytical predictive models and ML techniques can be utilised to provide a personalised model of the risk associated with the diseases. Based on this model, personalised treatment plans and recommendations can be derived by maximising the treatment outcome while simultaneously minimising patient risk.  
By collecting and analysing large volumes of patient data, it may be possible to identify underlying patterns that could prove instrumental to improving the quality of life for a large group of people. To achieve the best possible decision support system, access to both EHR data and patient-generated data is important. Both data sources have the potential to support the search for a successful decision support tool. Typically, overthe short term, it is easier and more realistic to obtain access to patient-generated data because we assume they contain fewer sensitive data. Information like heart rate, physical activity, sleep, medication, and patient-reported pain level may offer valuable insights into the patients' health under given circumstances. Such data may be used to determine whether the observed data can be used to quantify a patient's level of pain. If so, using these data to build a ML model that can provide tailored recommendations patients may reduce the number of painful episodes.  
RL and DTs may be utilised to build a data-driven decision support tool for the treatment and management of chronic pain. RL is an area of ML concerned with how to take optimal actions in an environment to maximise the notion of cumulative reward. Novel RL research needs to be conducted to make the algorithm safer and more robust and to reduce the risk for patients. DT is another machine learning technique that can be effective to visually and explicitly derive decisions and decision-making policies based on data.  
To enable the development and usability of personalised decision support systems, the availability of patient-generated data is crucial. By utilising new self-management technologies, personal data can be collected in a straightforward and cost-effective manner. ML algorithms can learn from these large amounts of personal data to customise treatment and management to the needs and preferences of each individual. However, a platform to collect and process these data in a privacy-preserving manner needs to be developed, to enable ML algorithms to exploit the vast potential of such data sources.