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Quantifying predictive capability of electronic health records for the most harmful breast cancer
Author(s): Yirong Wu; Jun Fan; Peggy Peissig; Richard Berg; Ahmad Pahlavan Tafti; Jie Yin; Ming Yuan; David Page; Jennifer Cox; Elizabeth S. Burnside
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Paper Abstract

Improved prediction of the “most harmful” breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the “most harmful” breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the “most harmful” breast cancer risk prediction. We identified 794 subjects who had breast cancer with primary non-benign tumors with their earliest diagnosis on or after 1/1/2004 from an existing personalized medicine data repository, including 395 “most harmful” breast cancer cases and 399 “least harmful” breast cancer cases. For these subjects, we collected EHR data comprised of 6 components: demographics, diagnoses, symptoms, procedures, medications, and laboratory results. We developed two regularized prediction models, Ridge Logistic Regression (Ridge-LR) and Lasso Logistic Regression (Lasso-LR), to predict the “most harmful” breast cancer one year in advance. The area under the ROC curve (AUC) was used to assess model performance. We observed that the AUCs of Ridge-LR and Lasso-LR models were 0.818 and 0.839 respectively. For both the Ridge-LR and LassoLR models, the predictive performance of the whole EHR variables was significantly higher than that of each individual component (p<0.001). In conclusion, EHR variables can be used to predict the “most harmful” breast cancer, providing the possibility to personalize care for those women at the highest risk in clinical practice.

Paper Details

Date Published: 7 March 2018
PDF: 9 pages
Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105770J (7 March 2018); doi: 10.1117/12.2293954
Show Author Affiliations
Yirong Wu, Univ. of Wisconsin-Madison (United States)
Jun Fan, Univ. of Wisconsin-Madison (United States)
Peggy Peissig, Marshfield Clinic (United States)
Richard Berg, Marshfield Clinic (United States)
Ahmad Pahlavan Tafti, Marshfield Clinic (United States)
Jie Yin, Jiangbei People's Hospital (China)
China Three Gorges Univ. (China)
Ming Yuan, Univ. of Wisconsin-Madison (United States)
David Page, Univ. of Wisconsin-Madison (United States)
Jennifer Cox, Univ. of Wisconsin-Madison (United States)
Elizabeth S. Burnside, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 10577:
Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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