  Sickle Cell Disease (SCD) is a chronic genetic disorder characterized by
recurrent acute painful episodes. Opioids are often used to manage these
painful episodes; the extent of their use in managing pain in this disorder is
an issue of debate. The risk of addiction and side effects of these opioid
treatments can often lead to more pain episodes in the future. Hence, it is
crucial to forecast future patient pain trajectories to help patients manage
their SCD to improve their quality of life without compromising their
treatment. It is challenging to obtain many pain records to design forecasting
models since it is mainly recorded by patients' self-report. Therefore, it is
expensive and painful (due to the need for patient compliance) to solve pain
forecasting problems in a purely supervised manner. In light of this challenge,
we propose to solve the pain forecasting problem using self-supervised learning
methods. Also, clustering such time-series data is crucial for patient
phenotyping, anticipating patients' prognoses by identifying "similar"
patients, and designing treatment guidelines tailored to homogeneous patient
subgroups. Hence, we propose a self-supervised learning approach for clustering
time-series data, where each cluster comprises patients who share similar
future pain profiles. Experiments on five years of real-world datasets show
that our models achieve superior performance over state-of-the-art benchmarks
and identify meaningful clusters that can be translated into actionable
information for clinical decision-making.
