Abstract
Background: Endometriosis affects 176 million women, with an average diagnostic delay of up to twelve years. We sought to evaluate differences between patients diagnosed with and without endometriosis, using electronic health record (EHR) data. Our goal was to use feature selection to determine the elements most predictive of endometriosis. Presented at: 16th World Congress on EndometriosisLocation: Sydney, AustriliaDates: May 21 to 24, 2025 Authors: Judith W. Dexheimer, PhD; Parand Shams, MS; Emily G. Hurley, MD; Katie Smith, WHNP; Rhonda Sczcesniak, PhD; Albert L. Hsu, MD Affiliations: Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical CenterDepartment of Pediatrics, College of Medicine, University of CincinnatiDepartment of Biostatistics, Health Informatics and Data Science, University of CincinnatiDivision of Reproductive Endocrinology and Infertility (REI), Department of Obstetrics and Gynecology, University of Cincinnati College of MedicineDivision of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical CenterDivision of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center
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Demographic and Clinical Profiles of Patients with Endometriosis
Authors/Creators
Description
Background:
Endometriosis affects 176 million women, with an average diagnostic delay of up to twelve years. We sought to evaluate differences between patients diagnosed with and without endometriosis, using electronic health record (EHR) data. Our goal was to use feature selection to determine the elements most predictive of endometriosis.
Presented at: 16th World Congress on Endometriosis
Location: Sydney, Austrilia
Dates: May 21 to 24, 2025
Authors: Judith W. Dexheimer, PhD; Parand Shams, MS; Emily G. Hurley, MD; Katie Smith, WHNP; Rhonda Sczcesniak, PhD; Albert L. Hsu, MD
Affiliations:
Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center
Department of Pediatrics, College of Medicine, University of Cincinnati
Department of Biostatistics, Health Informatics and Data Science, University of Cincinnati
Division of Reproductive Endocrinology and Infertility (REI), Department of Obstetrics and Gynecology, University of Cincinnati College of Medicine
Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center
Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center
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