JAMIA open · 2026
Computational phenotyping of sexually transmitted infections with the All of Us Research Program from 2010 to 2023.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Shi Fanghui, Xia Huiyi, Weissman Sharon, Li Xiaoming, Yang Xueying
Аннотация
OBJECTIVES: This study aims to develop comprehensive computable phenotyping algorithms that integrate multiple domains of electronic health record (EHR) data within the All of Us (AoU) Researcher Workbench to identify sexually transmitted infection (STI) cases and characterize STI patterns.
MATERIALS AND METHODS: We used AoU controlled tier data, version 8, which included participants enrolled from May 6, 2018, to October 1, 2023. Using data across multiple domains of EHRs in AoU, such as diagnostic codes, laboratory results, and medication records, we developed computational phenotyping algorithms to identify 3 leading STIs: chlamydia, gonorrhea, and syphilis.
RESULTS: Among 393 596 participants with EHR data in AoU, 2603 had confirmed chlamydia, 1520 had gonorrhea, and 2762 had syphilis. By utilizing information from diagnostic codes and medication records, we identified an additional 4843 individuals with possible/presumed chlamydia and 18 855 individuals with possible/presumed gonorrhea. Among 5784 participants with at least one confirmed STI, 115 (2.0%) had all 3 infections. A notable shift in STI trends occurred in 2019, with the occurrence increasing from 2010 to 2019 before declining through 2023 across different STIs.
DISCUSSION: The comprehensive computable phenotype algorithms developed in this study provide an enhanced approach to identifying STI cases that may be missed using traditional lab-based methods.
CONCLUSION: The computable phenotypes developed in this study provide a practical framework for more nuanced STI classification and downstream analysis in All of Us. Our approach may also support clinical and public health efforts by improving case identification beyond laboratory-only methods and informing interventions addressing clinical and social needs.
MATERIALS AND METHODS: We used AoU controlled tier data, version 8, which included participants enrolled from May 6, 2018, to October 1, 2023. Using data across multiple domains of EHRs in AoU, such as diagnostic codes, laboratory results, and medication records, we developed computational phenotyping algorithms to identify 3 leading STIs: chlamydia, gonorrhea, and syphilis.
RESULTS: Among 393 596 participants with EHR data in AoU, 2603 had confirmed chlamydia, 1520 had gonorrhea, and 2762 had syphilis. By utilizing information from diagnostic codes and medication records, we identified an additional 4843 individuals with possible/presumed chlamydia and 18 855 individuals with possible/presumed gonorrhea. Among 5784 participants with at least one confirmed STI, 115 (2.0%) had all 3 infections. A notable shift in STI trends occurred in 2019, with the occurrence increasing from 2010 to 2019 before declining through 2023 across different STIs.
DISCUSSION: The comprehensive computable phenotype algorithms developed in this study provide an enhanced approach to identifying STI cases that may be missed using traditional lab-based methods.
CONCLUSION: The computable phenotypes developed in this study provide a practical framework for more nuanced STI classification and downstream analysis in All of Us. Our approach may also support clinical and public health efforts by improving case identification beyond laboratory-only methods and informing interventions addressing clinical and social needs.