用1.4亿患者数据训练的医疗大模型，能更准预测医疗费用和慢性病风险。
Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences
- 基于患者医疗事件序列训练，采用专业医学术语词表的生成式Transformer模型。
- 成本预测比最优商用模型提升14.1%，慢性病预测比最优研究模型提升1.9%。
- 适合医疗决策支持、医保控费和个性化健康管理场景使用。
美国医疗支出接近5万亿美元（2024年国家健康支出简报），其中约25%被估计为浪费。本文提出大型医疗模型（LMM），一种基于患者事件序列预训练的生成式Transformer（GPT）模型，用于指导和预测患者护理及医疗管理的多个方面。该模型在超过1.4亿份纵向患者索赔记录上进行训练，使用源自医学术语系统的专用词汇表，展现出卓越的医疗成本预测与风险因素识别能力。实验验证表明，LMM不仅在成本与风险预测上表现优异，还能揭示复杂疾病中的深层模式，并发现患者护理中的新关系。相比最优商用模型，其成本预测性能提升14.1%；在预测广泛疾病方面，比最优研究级Transformer模型提升1.9%。LMM显著推进了医疗数据分析，有望大幅提升风险评估、成本管控与精准医疗水平。
原文摘要 · Abstract (English)
With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical Model (LMM), a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration. The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary built from medical terminology systems and demonstrates a superior capability to forecast healthcare costs and identify potential risk factors. Through experimentation and validation, we showcase the LMM's proficiency in not only in cost and risk predictions, but also in discerning intricate patterns within complex medical conditions and an ability to identify novel relationships in patient care. The LMM is able to improve both cost prediction by 14.1% over the best commercial models and chronic conditions prediction by 1.9% over the best transformer models in research predicting a broad set of conditions. The LMM is a substantial advancement in healthcare analytics, offering the potential to significantly enhance risk assessment, cost management, and personalized medicine.
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。