Data Augmentation for Classification of Negative Pregnancy Outcomes in Imbalanced Data

TL;DR

This paper presents a novel NLP-based data augmentation approach using social media data to improve classification of negative pregnancy outcomes in imbalanced datasets, aiding epidemiological research.

Contribution

It introduces a new NLP pipeline for identifying and categorizing pregnancy experiences from social media, addressing data imbalance and noise challenges.

Findings

Effective identification of women sharing pregnancy experiences

Enhanced dataset quality for negative pregnancy outcomes

Potential for improved epidemiological analysis

Abstract

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.