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.

镰状细胞病（SCD）是一种慢性遗传性疾病，其特征为反复急性疼痛发作。阿片类药物常用于处理这些疼痛发作；然而，关于在这种疾病中使用阿片类药物治疗疼痛的程度存在争议。药物成瘾风险和副作用经常导致未来疼痛发作增多。因此，为帮助患者管理SCD以提高生活质量而不损害治疗，需要预测未来病人的疼痛轨迹。本文提出使用自我监督学习方法解决疼痛预测问题，同时进行时间序列数据的聚类，以实现病人表型化，预测病人预后，并为临床决策制定相适应的治疗指南。实验结果显示，与最先进的基准模型相比，我们的模型在为期五年的实际数据集上表现出卓越的性能，并识别出有意义的簇，可转化为可应用的临床决策信息。

利用自我监督学习和患者表型建模的疼痛预测：预防阿片类药物成瘾的尝试