Demographic and Clinical Profiles of Patients with Endometriosis

Judith W. Dexheimer, Parand Shams, Emily G. Hurley, Katie E. Smith, Sczcesniak Rhonda, Albert Hsu
2025 · doi:10.5281/zenodo.19473031 · W7152136231
article OA: green CC0
🔓 Open OA copy Full text JSON View on OpenAlex View at publisher
⚙ AI-generated summary by qwen3.7-flash, 2026-09-01 ⓘ

This study analyzed electronic health record data to identify demographic and clinical features most predictive of endometriosis diagnosis, aiming to address the significant diagnostic delay associated with the condition.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by qwen3.7-flash, 2026-09-01 · read from full text ⓘ

This study utilized electronic health record data to evaluate demographic and clinical differences between patients diagnosed with endometriosis and those without. The researchers employed feature selection techniques to identify the specific elements most predictive of an endometriosis diagnosis within their dataset. By analyzing these patterns, the work aims to address the significant diagnostic delay often experienced by women with this condition. This paper is centrally about endometriosis — specifically focusing on developing predictive models for diagnosis using clinical data.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

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
Full text 1,410 characters · extracted from oa-doi-fallback · click to expand
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 Files Abstract_WECAus.pdf Files (697.3 kB) | Name | Size | Download all | |---|---|---| | md5:4ba15ae918c3857013f167ed4d03398b | 697.3 kB | Preview Download |

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Condition tags

endometriosisinfertility

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

openalex
last seen: 2026-06-10T17:14:06.276822+00:00
License: CC0 · commercial use OK