Cluster-Then-Classify Methodology for the Identification of Pain Episodes in Chronic Diseases

Chronic diseases benefit of the advances on personalize medicine coming out of the integrative convergence of significant developments in systems biology, the Internet of Things and Artificial Intelligence. 70% to 80% of all healthcare costs in the EU and US are currently spent on chronic diseases, leading to estimated costs of
${\rm C}\!\!\!\!\!\!\!=$
700 billion and
${\$}$
3.5 trillion respectively. The management of symptomatic pain crises in chronic diseases is based on general clinical guidelines that do not take into account the singularities of the crises, such as their intensity or duration, so that the pain of those particular crises may cause the medication to be ineffective and lead the patient to overmedication. Knowing in detail the characteristics of the pain would help the physician to objectively prescribe personalized treatments for each patient and crisis. In this manuscript, we make a step further on the prediction of symptomatic crisis from ambulatory collected data in chronic diseases. We propose a categorization of pain types according to subjective symptoms of real patients. Our approach has been evaluated in the migraine disease. The migraine is one of the most disabling neurological diseases that affects over 12% of the population worldwide and leads to high economic costs for private and public health systems. This study aims to classify pain episodes by the characterization of pain curves reported by patients in real time. Pain curves have been described as a set of morphological features. With these features the pain episodes are clustered then classified by unsupervised and supervised machine learning models. It is shown that the evolution of different pain episodes in chronic diseases can be modeled and clustered. Over a population of 51 migraine patients, it has been found that there are 4 clusters of pain types that can be classified using 4 morphological features with an accuracy of 99.0% using a Logistic Model Tree algorithm.