Risk and strength: determining the impact of area-level racial bias and protective factors on birth outcomes

项目摘要 我们的母公司Grant专注于使用深度机器学习模型来分析社交媒体数据的文本， 建立地区一级的种族情绪衡量标准，并将其与出生结果联系起来。我们只专注于 基于种族的偏见，并没有分析偏见的视觉表现。这项工作中有希望的发现 提出了关于这种方法在调查其他健康差异方面的普遍性的问题 比如性别和性取向我们还寻求开发新的AI/ML 从基于图像的内容中获得情感的方法，这是一种越来越常见的形式， 社交媒体上的沟通。行政补充将允许我们通过以下方式推进AI/ML领域 使数据AI/ML为图像分析做好准备，训练包含文本和 图像分析，并创造宝贵的资源，将缩短所需的时间和专业知识， 实施机器学习模型，以调查地区一级的偏见对卫生不公平的影响。我们将 通过为交互式数据创建公共地理门户，分享我们的AI/ML社交媒体衍生措施 可视化和共享，其中将包含汇总数据的存储库，这将有助于使用这个大的 数据来源，供今后在健康公平研究中应用。

PROJECT SUMMARY Our parent grant focused on using deep machine learning models to analyze the text of social media data to create area-level measures of racial sentiment and to link them with birth outcomes. We focused solely on race-based biases and did not analyze visual representations of bias. The promising findings from this work open up questions about the generalizability of this approach for investigating additional health disparities across other intersecting identities like gender and sexual orientation. We also seek to develop new AI/ML methods to derive sentiment from image-based content, which is an increasingly common form of communication on social media. The administrative supplement will allow us to advance this AI/ML area by making the data AI/ML ready for image analysis, training multimodal models that incorporate both text and image analysis, and creating valuable resources that will shorten the time and specialized expertise needed to implement machine learning models to investigate the impact of area-level biases on health inequities. We will be sharing our AI/ML-ready social media-derived measures by creating a public geoportal for interactive data visualization and sharing, which will contain repositories of aggregated data that will facilitate the use of this big data source for future applications in health equity research.