Learning endometriosis phenotypes from patient-generated data

Abstract

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.

Predictors of Psychological Distress in Women with Endometriosis: The Role of Multimorbidity, Body Image, and Self-Criticism

“…It may not be endometriosis per se that is responsible for depression and anxiety but rather the experience of comorbidities that do not merely produce an additive burden but interact and, in some instances, result in magnified effects [ 49 ]. This direction may be supported by the findings of Urteaga et al [ 54 ], who used patient-generated health data and data-driven phenotyping, based mainly on reported signs and symptoms, to characterize four subtypes of endometriosis patients. Specifically, they described Phenotype A as a particularly severe endometriosis subtype with symptoms related to several comorbidities such as anxiety, depression, and other mood disorders, migraines, high blood pressure, and chronic fatigue syndrome.…”

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

“…Participants can track free-text responses to these two items, which then get mapped onto common terms for standardization (e.g., “go to a family gathering,” “hang out with friends” get mapped onto “socialize”). This standardization process has been used in other previous research from our group, 71 76 which we maintain for this analysis, and rely on published literature 43 52 for generalizability and comparability of the prevalence estimates.…”

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care

“…Another rate-limiting factor could be the continued stigmatization around MH disorder which may prevent patients with these issues from presenting to services. Digital medicine and PM approaches combined with AI could provide methods to identify prevalence data more comprehensively 91 and provide interventions at a fraction of the cost. Using PM methods, patients would be able to self-report and transmit this data to a clinician using digital solutions such as a simple mobile application.…”