It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data.

中文翻译：

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打开黑匣子：根据潜在表型和时间相关并发症规则个性化 2 型糖尿病患者

人们普遍认为，大约 10% 的人口患有 2 型糖尿病。不幸的是，这种疾病的影响被低估了。患者的死亡往往是由于疾病引起的并发症，而不是疾病本身。许多用于疾病建模的技术通常以“黑匣子”的形式出现，其中的内部工作原理和复杂性极难理解，无论是从从业者的角度还是从患者的角度。在这项工作中，我们解决了这个问题并提出了一个信息模型/模式，称为“潜在表型”，旨在捕捉相关并发症的复杂性。我们通过结合使用时间关联规则挖掘和无监督学习来进一步扩展这一想法，以便找到具有更个性化预测的可解释患者亚组。我们广泛的研究结果表明，揭示潜在表型如何有助于根据并发症模式区分患者亚组之间的差异。我们深入了解如何最好地提高预测性能并减少使用患者数据中的不确定性应用的模型中的偏差。