Infant mortality remains a significant public health concern in the United States, with birth defects identified as a leading cause. Despite ongoing efforts to understand the causes of negative pregnancy outcomes like miscarriage, stillbirths, birth defects, and premature birth, there is still a need for more comprehensive research and strategies for intervention. This paper introduces a novel approach that uses publicly available social media data, especially from platforms like Twitter, to enhance current datasets for studying negative pregnancy outcomes through observational research. The inherent challenges in utilizing social media data, including imbalance, noise, and lack of structure, necessitate robust preprocessing techniques and data augmentation strategies. By constructing a natural language processing (NLP) pipeline, we aim to automatically identify women sharing their pregnancy experiences, categorizing them based on reported outcomes. Women reporting full gestation and normal birth weight will be classified as positive cases, while those reporting negative pregnancy outcomes will be identified as negative cases. Furthermore, this study offers potential applications in assessing the causal impact of specific interventions, treatments, or prenatal exposures on maternal and fetal health outcomes. Additionally, it provides a framework for future health studies involving pregnant cohorts and comparator groups. In a broader context, our research showcases the viability of social media data as an adjunctive resource in epidemiological investigations about pregnancy outcomes.

翻译：在美国，婴儿死亡率仍然是一个重大的公共卫生问题，其中出生缺陷被确定为主要原因。尽管人们持续努力理解如流产、死产、出生缺陷和早产等负向妊娠结局的成因，但仍需要更全面的研究和干预策略。本文提出了一种新颖方法，利用公开的社交媒体数据，特别是来自Twitter等平台的数据，通过观察性研究来增强现有用于研究负向妊娠结局的数据集。利用社交媒体数据固有的挑战，包括不平衡性、噪声和缺乏结构性，需要强大的预处理技术和数据增强策略。通过构建一个自然语言处理（NLP）流程，我们的目标是自动识别分享其妊娠经历的女性，并根据报告的结果对她们进行分类。报告足月妊娠和正常出生体重的女性将被归类为阳性案例，而报告负向妊娠结局的女性将被识别为阴性案例。此外，本研究为评估特定干预措施、治疗方法或产前暴露对母婴健康结局的因果影响提供了潜在应用。同时，它为未来涉及妊娠队列和对照组的健康研究提供了一个框架。在更广泛的背景下，我们的研究展示了社交媒体数据作为妊娠结局流行病学调查辅助资源的可行性。