TY - JOUR AU - Sehgal, Neil Jay AU - Huang, Shuo AU - Johnson, Neil Mason AU - Dickerson, John AU - Jackson, Devlon AU - Baur, Cynthia PY - 2022 DA - 2022/6/21 TI -众包在非裔美国人和西班牙裔成年人移动医疗干预中种子和对齐算法的好处:调查研究JO - J Med Internet Res SP - e30216 VL - 24 IS - 6kw -众包KW -健康信息KW -健康促进KW -预防KW -公共卫生信息学KW -非裔美国人、黑人、拉丁裔和西班牙裔人群KW -推荐系统KW - RecSys KW -机器学习KW - Mechanical Turk KW - MTurk KW -移动电话AB -背景:缺乏关于非裔美国人和说双语/西班牙语的西班牙裔成年人疾病预防和健康促进优先事项的公开可用和文化相关数据集，这对希望创建和测试基于这些优先事项并与之一致的个性化工具的研究人员和开发人员提出了重大挑战。个性化依赖于预测和性能数据。一个推荐系统(RecSys)可以预测最具文化和个人相关性的预防健康信息，并通过一款新颖的智能手机应用程序将其提供给非裔美国人和西班牙裔用户。然而，在用户体验的早期，RecSys可能会面临“冷启动问题”，即在了解用户偏好之前，提供未经定制和不相关的内容。对于服务不足的非裔美国人和西班牙裔人群来说，他们一直在接受针对白人多数的健康内容的服务，冷启动问题可能成为算法偏见的一个例子。为了避免这种情况，RecSys需要与应用程序的目的相一致的适合人群的种子数据。众包提供了一种生成适合人群的种子数据的方法。目的:我们的目标是确定并测试一种方法，以解决缺乏文化特异性预防性个人健康数据的问题，并避免未在重点人群中接受培训的RecSys固有的算法偏差。为此，我们快速、低成本地从重点人群中收集了大量数据，从而生成了一套基于以预防为重点、与人群相关的健康目标的新数据集。 We seeded our RecSys with data collected anonymously from self-identified Hispanic and self-identified non-Hispanic African American/Black adult respondents, using Amazon Mechanical Turk (MTurk). Methods: MTurk provided the crowdsourcing platform for a web-based survey in which respondents completed a personal profile and a health information–seeking assessment, and provided data on family health history and personal health history. Respondents then selected their top 3 health goals related to preventable health conditions, and for each goal, reviewed and rated the top 3 information returns by importance, personal utility, whether the item should be added to their personal health library, and their satisfaction with the quality of the information returned. This paper reports the article ratings because our intent was to assess the benefits of crowdsourcing to seed a RecSys. The analysis of the data from health goals will be reported in future papers. Results: The MTurk crowdsourcing approach generated 985 valid responses from 485 (49%) self-identified Hispanic and 500 (51%) self-identified non-Hispanic African American adults over the course of only 64 days at a cost of US $6.74 per respondent. Respondents rated 92 unique articles to inform the RecSys. Conclusions: Researchers have options such as MTurk as a quick, low-cost means to avoid the cold start problem for algorithms and to sidestep bias and low relevance for an intended population of app users. Seeding a RecSys with responses from people like the intended users allows for the development of a digital health tool that can recommend information to users based on similar demography, health goals, and health history. This approach minimizes the potential, initial gaps in algorithm performance; allows for quicker algorithm refinement in use; and may deliver a better user experience to individuals seeking preventative health information to improve health and achieve health goals. SN - 1438-8871 UR - //www.mybigtv.com/2022/6/e30216 UR - https://doi.org/10.2196/30216 UR - http://www.ncbi.nlm.nih.gov/pubmed/35727616 DO - 10.2196/30216 ID - info:doi/10.2196/30216 ER -