Social determinants of health (SDOH) play a critical role in Type 2 Diabetes (T2D) management but are often absent from electronic health records and risk prediction models. Most individual-level SDOH data is collected through structured screening tools, which lack the flexibility to capture the complexity of patient experiences and unique needs of a clinic's population. This study explores the use of large language models (LLMs) to extract structured SDOH information from unstructured patient life stories and evaluate the predictive value of both the extracted features and the narratives themselves for assessing diabetes control. We collected unstructured interviews from 65 T2D patients aged 65 and older, focused on their lived experiences, social context, and diabetes management. These narratives were analyzed using LLMs with retrieval-augmented generation to produce concise, actionable qualitative summaries for clinical interpretation and structured quantitative SDOH ratings for risk prediction modeling. The structured SDOH ratings were used independently and in combination with traditional laboratory biomarkers as inputs to linear and tree-based machine learning models (Ridge, Lasso, Random Forest, and XGBoost) to demonstrate how unstructured narrative data can be applied in conventional risk prediction workflows. Finally, we evaluated several LLMs on their ability to predict a patient's level of diabetes control (low, medium, high) directly from interview text with A1C values redacted. LLMs achieved 60% accuracy in predicting diabetes control levels from interview text. This work demonstrates how LLMs can translate unstructured SDOH-related data into structured insights, offering a scalable approach to augment clinical risk models and decision-making.

翻译：社会健康决定因素（SDOH）在2型糖尿病（T2D）管理中起着关键作用，但电子健康记录和风险预测模型通常缺乏这些信息。大多数个体层面的SDOH数据通过结构化筛查工具收集，这些工具缺乏灵活性，难以捕捉患者经历的复杂性及特定临床人群的独特需求。本研究探索利用大语言模型（LLMs）从非结构化的患者生活故事中提取结构化SDOH信息，并评估所提取特征及叙事本身对糖尿病控制状况的预测价值。我们收集了65名65岁及以上T2D患者的非结构化访谈，内容聚焦于其生活经历、社会背景和糖尿病管理。通过采用检索增强生成技术的LLMs对这些叙事进行分析，生成了用于临床解读的简明、可操作的定性总结，以及用于风险预测建模的结构化定量SDOH评分。这些结构化SDOH评分被独立使用，并与传统实验室生物标志物结合，作为线性及基于树的机器学习模型（Ridge、Lasso、随机森林和XGBoost）的输入，以展示非结构化叙事数据如何应用于传统的风险预测工作流程。最后，我们评估了多种LLM直接从访谈文本（其中A1C值已被隐去）预测患者糖尿病控制水平（低、中、高）的能力。LLMs在仅依据访谈文本预测糖尿病控制水平方面达到了60%的准确率。这项工作展示了LLMs如何将非结构化的SDOH相关数据转化为结构化洞见，为增强临床风险模型和决策提供了一种可扩展的方法。