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

Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction