用推理模型从病历中预测患者社会健康因素编码，准确率达89%。
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
- 结合推理模型与大语言模型，识别住院记录中的社会健康因素。
- 在MIMIC-III数据集上实现89%的F1分数，发现139次入院遗漏社会因素编码。
- 结果可复现，适合医疗人工智能与健康公平性研究者参考。
社会健康决定因素（SDoH）与患者预后相关，但常未被结构化数据捕捉。近年来，研究聚焦于从临床文本自动提取这些特征，以补充诊断系统对患者社会背景的认知。大型语言模型在句子级社会健康因素标签识别中表现优异，但在长篇入院记录或纵向病历中因长距离依赖而面临挑战。本文基于MIMIC-III数据集，探索使用推理模型与传统大语言模型进行住院记录多标签社会健康因素ICD-9编码分类。利用已有ICD-9编码进行预测，达到89%的F1分数。研究发现139例入院记录中存在缺失的SDoH编码，并公开代码供复现。
原文摘要 · Abstract (English)
Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of patients' social circumstances. Large language models demonstrate strong performance in identifying Social Determinants of Health labels from sentences. However, prediction in large admissions or longitudinal notes is challenging given long distance dependencies. In this paper, we explore hospital admission multi-label Social Determinants of Health ICD-9 code classification on the MIMIC-III dataset using reasoning models and traditional large language models. We exploit existing ICD-9 codes for prediction on admissions, which achieved an 89% F1. Our contributions include our findings, missing SDoH codes in 139 admissions, and code to reproduce the results.
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