Sociophysics of subjective pain: robust cluster assignment based on sparsely and irregularly sampled data from a dynamical system

Prior research has attempted to explain changes in pain levels over time using differential equation based models. In this work, we use techniques drawn from the study of complex networks to motivate partitioning of patient populations into distinct clusters. We introduce and test a variety of methods for comparing irregularly and sparsely sampled trajectories, with the goal of providing a rigorous basis for defining clusters and assigning trajectories to them.

We apply our methods to a dataset from individuals with chronic pain caused by sickle cell disease, a genetic blood disorder that impacts over 100,000 Americans and over 4.4 people worldwide. The dataset, drawn from 39 distinct patients, includes 221 total pain trajectories lasting 14 days each. By gaining a better quantitative understanding of patients' differing experiences of pain, we aim to generate recommendations that help better manage pain levels over time and to improve our ability to forecast future pain. The methods developed in this work should also have further application to a variety of problems involving sparsely and irregularly sampled data.