Method used to identify adenomyosis and potentially undiagnosed adenomyosis in a large, U.S. electronic health record database
This study developed an NLP method to identify adenomyosis cases and estimate the prevalence of potentially undiagnosed adenomyosis in a large EHR database, finding the total prevalence may be 10x higher than previously estimated.
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References (22)
- Adenomyosis: A Clinical Review of a Challenging Gynecologic Condition via openalex
- Incidence and Estimated Prevalence of Endometriosis and Adenomyosis in Northeast Italy: A Data Linkage Study via openalex
- MRI for adenomyosis: a pictorial review via openalex
- Surgery: Adenomyosis at hysterectomy: a study on frequency distribution and patient characteristics via openalex
- Ultrasound scan and magnetic resonance imaging for the diagnosis of adenomyosis: systematic review comparing test accuracy via openalex
- Uterine Adenomyosis: Endovaginal US and MR Imaging Features with Histopathologic Correlation via openalex
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Cited by (5)
- Epidemiological characteristics of suspected adenomyosis in the Chinese physical examination population: a nested case-control study 2024
- Society of Endometriosis and Uterine Disorders forum: adenomyosis today, Paris, France, December 12, 2023 2024
- Prevalence of adenomyosis in symptomatic adolescents and young women: a systematic review and meta-analysis 2024
- Connecting the Dots on Female Digital Health Education: A Systematic Review 2024
- The Prevalence and Clinical Impact of Adenomyosis in Pregnancy-Related Hysterectomy 2022
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