融合LLM与传统深度学习，提升健康影响因素预测效率与准确率
Integration of Large Language Models and Traditional Deep Learning for Social Determinants of Health Prediction
- 结合LLM精度与传统模型效率，设计混合预测框架
- 多标签分类准确率提升10个百分点，推理速度加快12倍
- 适用于医疗风险患者早期筛查，兼顾性能与部署成本
社会决定健康因素（SDoH）是影响个体健康状况的经济、社会及个人背景因素，与健康结果密切相关，对疾病诊断和临床决策具有重要价值。本文通过传统深度学习与大语言模型（LLMs）从临床文本中自动提取SDoH，评估二者在公开数据集上的优劣。所提模型在多标签SDoH分类任务上较先前基准提升10个百分点，并提出一种新方法，通过移除高成本的LLM处理环节，使执行时间缩短12倍。该方法结合了LLM的高精度与传统深度学习的高效性。此外，在加入合成数据的扩充数据集上，多种传统深度学习模型表现优于LLMs。本研究为高风险患者的SDoH自动预测提供了更高效、实用的新范式。
原文摘要 · Abstract (English)
Social Determinants of Health (SDoH) are economic, social and personal circumstances that affect or influence an individual's health status. SDoHs have shown to be correlated to wellness outcomes, and therefore, are useful to physicians in diagnosing diseases and in decision-making. In this work, we automatically extract SDoHs from clinical text using traditional deep learning and Large Language Models (LLMs) to find the advantages and disadvantages of each on an existing publicly available dataset. Our models outperform a previous reference point on a multilabel SDoH classification by 10 points, and we present a method and model to drastically speed up classification (12X execution time) by eliminating expensive LLM processing. The method we present combines a more nimble and efficient solution that leverages the power of the LLM for precision and traditional deep learning methods for efficiency. We also show highly performant results on a dataset supplemented with synthetic data and several traditional deep learning models that outperform LLMs. Our models and methods offer the next iteration of automatic prediction of SDoHs that impact at-risk patients.
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