.png){kind=icon}
返回
Learning endometriosis phenotypes from patient-generated data
.png){kind=icon}
OA
AI
DOI：10.1038/s41746-020-0292-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
POLYCYSTIC-OVARY-SYNDROME
CHRONIC-FATIGUE-SYNDROME
QUALITY-OF-LIFE
WOMEN
POPULATION
PREVALENCE
ASSOCIATION
SEVERITY
SYMPTOMS
IMPACT
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.1
论文数:
3.3K
被引数:
1.5W
机构
引用论文
分享收藏Surgical versus medical treatment for endometriosis-associated severe deep dyspareunia: I. Effect on pain during intercourse and patient satisfaction.png)
HUMAN REPRODUCTIONOAAI
HUMAN REPRODUCTIONIF6.1
OAAI分享收藏The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKitNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏The Prevalence of Fibromyalgia in the General Population A Comparison of the American College of Rheumatology 1990, 2010, and Modified 2010 Classification CriteriaARTHRITIS & RHEUMATOLOGYOAAI
ARTHRITIS & RHEUMATOLOGYIF10.9
OAAI分享收藏分享收藏
查看更多内容