零样本大型语言模型能否预测儿童营养不良？一项关于公平性与时序鲁棒性的研究
Can Zero-Shot LLMs Predict Child Malnutrition? A Fairness and Temporal Robustness Study
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中文总结 AI 辅助
本研究评估零样本LLM预测儿童营养不良的可行性，发现GPT-4o-mini零样本预测儿童发育迟缓性能接近随机森林基线，灵敏度更高、性别公平性好、时序稳定，但居住地和家庭财富维度存在公平性差异，需进一步研究。
中文摘要 AI 辅助
儿童营养不良仍是中低收入国家，尤其是南亚地区的重大公共卫生挑战，早期识别弱势儿童对及时干预和资源分配至关重要。本研究旨在评估在零样本设置下使用预训练大型语言模型（LLM）通过人口健康调查数据预测儿童发育迟缓的可行性、公平性与时序鲁棒性。我们使用2007至2022年间收集的孟加拉国人口与健康调查（BDHS）数据，将孕产妇、儿童、医疗保健及家庭特征转化为语义可解释的基于提示的表示，评估GPT-4o-mini用于零样本发育迟缓预测的性能，将其与随机森林基线模型对比，并评估其在人口统计学与社会经济群体间的公平性以及不同调查周期间的时序鲁棒性。结果显示，GPT-4o-mini的零样本推理达到了与监督基线相当的平衡准确率，同时在识别发育迟缓病例时表现出显著更高的灵敏度，在不同儿童性别群体间性能相对一致，在各BDHS调查周期间预测行为稳定；但在居住地和家庭财富类别间存在显著公平性差异，这表明在将基础模型部署到公共卫生预测场景前需进一步研究。
英文摘要
Child malnutrition remains a major public health challenge in low- and middle-income countries, particularly in South Asia, where early identification of vulnerable children is critical for timely intervention and resource allocation. This study aims to evaluate the feasibility, fairness, and temporal robustness of using a pretrained large language model (LLM) in a zero-shot setting for child stunting prediction using population health survey data. Using Bangladesh Demographic and Health Survey (BDHS) data collected between 2007 and 2022, we transformed maternal, child, healthcare, and household characteristics into semantically interpretable prompt-based representations and evaluated GPT-4o-mini for zero-shot stunting prediction, comparing its performance against a random forest baseline and assessing fairness across demographic and socioeconomic groups as well as temporal robustness across survey waves. The results demonstrate that zero-shot inference using GPT-4o-mini achieved comparable balanced accuracy to the supervised baseline while exhibiting substantially higher sensitivity for identifying stunting cases, relatively consistent performance across child sex groups, and stable predictive behaviour across BDHS waves; however, important fairness disparities were observed across residence and household wealth categories, highlighting the need for further investigation before deployment of foundation models in public health prediction settings.
发表机构
- School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯斯特大学计算、数学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
