用电子病历和社交风险预测退伍军人未来一年首次无家可归，精准度提升三成。
Predicting first-episode homelessness among US Veterans using longitudinal EHR data: time-varying models and social risk factors
- 构建动态电子病历模型，融合临床与社会风险随时间变化的轨迹。
- 在最高1%高风险人群中，12个月预测阳性率可达11.65%-13.80%。
- 模型可识别高危群体，适合用于退伍军人事务部的早期干预决策。
美国退伍军人无家可归仍是重大公共健康问题，风险预测为提前干预提供可能。本回顾性预后研究分析了2016年427万6403名退伍军人事务患者电子健康记录（EHR）数据，预测2017年3-12个月内首次无家可归事件（患病率0.32%-1.19%）。构建静态与动态EHR表示，采用临床医生指导的逻辑建模慢性病与社会风险持续性。比较经典机器学习、基于Transformer的掩码语言模型及微调大语言模型（LLMs）性能。结果显示，在纵向模型中引入社会行为因素使精确率-召回率曲线下面积（PR-AUC）提升15%-30%。在前1%高风险人群，模型在3个月、6个月、9个月和12个月的阳性预测值分别为3.93%-4.72%、7.39%-8.30%、9.84%-11.41%、11.65%-13.80%。大语言模型在判别能力上弱于编码器模型，但在不同种族间表现差异更小。结果表明，结合社会因素的长期病历建模可将风险集中于可行动层级，支持针对高危退伍军人的数据驱动预防策略。
原文摘要 · Abstract (English)
Homelessness among US veterans remains a critical public health challenge, yet risk prediction offers a pathway for proactive intervention. In this retrospective prognostic study, we analyzed electronic health record (EHR) data from 4,276,403 Veterans Affairs patients during a 2016 observation period to predict first-episode homelessness occurring 3-12 months later in 2017 (prevalence: 0.32-1.19%). We constructed static and time-varying EHR representations, utilizing clinician-informed logic to model the persistence of clinical conditions and social risks over time. We then compared the performance of classical machine learning, transformer-based masked language models, and fine-tuned large language models (LLMs). We demonstrate that incorporating social and behavioral factors into longitudinal models improved precision-recall area under the curve (PR-AUC) by 15-30%. In the top 1% risk tier, models yielded positive predictive values ranging from 3.93-4.72% at 3 months, 7.39-8.30% at 6 months, 9.84-11.41% at 9 months, and 11.65-13.80% at 12 months across model architectures. Large language models underperformed encoder-based models on discrimination but showed smaller performance disparities across racial groups. These results demonstrate that longitudinal, socially informed EHR modeling concentrates homelessness risk into actionable strata, enabling targeted and data-informed prevention strategies for at-risk veterans.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。