Endometriosis is a systemic and chronic condition in women of childbearing age, yet a highly enigmatic disease with unresolved questions: there are no known biomarkers, nor established clinical stages. We here investigate the use of patient-generated health data and data-driven phenotyping to characterize endometriosis patient subtypes, based on their reported signs and symptoms. We aim at unsupervised learning of endometriosis phenotypes using self-tracking data from personal smartphones. We leverage data from an observational research study of over 4000 women with endometriosis that track their condition over more than 2 years. We extend a classical mixed-membership model to accommodate the idiosyncrasies of the data at hand, i.e., the multimodality and uncertainty of the self-tracked variables. The proposed method, by jointly modeling a wide range of observations (i.e., participant symptoms, quality of life, treatments), identifies clinically relevant endometriosis subtypes. Experiments show that our method is robust to different hyperparameter choices and the biases of self-tracking data (e.g., the wide variations in tracking frequency among participants). With this work, we show the promise of unsupervised learning of endometriosis subtypes from self-tracked data, as learned phenotypes align well with what is already known about the disease, but also suggest new clinically actionable findings. More generally, we argue that a continued research effort on unsupervised phenotyping methods with patient-generated health data via new mobile and digital technologies will have significant impact on the study of enigmatic diseases in particular, and health in general.

子宫内膜异位症是育龄妇女的一种全身性慢性疾病，但却是一种高度神秘的疾病，其问题尚未解决：没有已知的生物标志物，也没有确定的临床阶段。我们在此研究如何使用患者生成的健康数据和数据驱动的表型分析，根据报告的体征和症状来表征子宫内膜异位症患者亚型。我们的目标是使用个人智能手机的自我跟踪数据对子宫内膜异位症表型进行无监督学习。我们利用一项对 4000 多名患有子宫内膜异位症的女性进行的观察性研究的数据，该研究跟踪了她们超过 2 年的病情。我们扩展了经典的混合成员模型以适应手头数据的特性，即自跟踪变量的多模态和不确定性。所提出的方法通过联合建模广泛的观察结果（即参与者症状、生活质量、治疗），识别临床相关的子宫内膜异位症亚型。实验表明，我们的方法对于不同的超参数选择和自我跟踪数据的偏差（例如，参与者之间跟踪频率的巨大差异）具有鲁棒性。通过这项工作，我们展示了从自我跟踪数据中无监督学习子宫内膜异位症亚型的前景，因为学习的表型与对该疾病的已知信息非常吻合，但也提出了新的临床可行的发现。更一般地说，我们认为，通过新的移动和数字技术对患者生成的健康数据进行无监督表型分析方法的持续研究将对特别是神秘疾病的研究和一般健康产生重大影响。