Design and Implementation of a Comprehensive Web-based Survey for Ovarian Cancer Survivorship with an Analysis of Prediagnosis Symptoms via Text Mining

Jiayang Sun ； Kath M. Bogie ； Joe Teagno ； Yu-Hsiang (Sam) Sun) ； Rebecca R. Carter ； Licong Cui ； Guo-Qiang Zhang

《Cancer Informatics》 2014-S3期 (2014/12) Pp. 113-123

https://doi.org/10.4137/CIN.S14034

並列摘要

Ovarian cancer (OvCa) is the most lethal gynecologic disease in the United States, with an overall 5-year survival rate of 44.5%, about half of the 89.2% for all breast cancer patients. To identify factors that possibly contribute to the long-term survivorship of women with OvCa, we conducted a comprehensive online Ovarian Cancer Survivorship Survey from 2009 to 2013. This paper presents the design and implementation of our survey, introduces its resulting data source, the OVA-CRADLE^(TM) (Clinical Research Analytics and Data Lifecycle Environment), and illustrates a sample application of the survey and data by an analysis of prediagnosis symptoms, using text mining and statistics. The OVA-CRADLETM is an application of our patented Physio- MIMI technology, facilitating Web-based access, online query and exploration of data. The prediagnostic symptoms and association of early-stage OvCa diagnosis with endometriosis provide potentially important indicators for future studies in this field.

並列關鍵字

survey ； design ； database ； informatics ； CRADLETM (Clinical Research Analytics and Data Lifecycle Environment) ； early diagnosis ； ovarian cancer ； endometriosis ； prediagnosis symptoms ； text mining ； correspondence analysis ； (CART) Classification and Regression Tree ； and Random Forest