大型医疗模型（LMM）介绍：基于患者事件序列训练的Transformer在医疗成本与风险预测中的最先进应用
Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences
AI总结：
针对美国医疗支出高且浪费严重的问题，本文提出大型医疗模型（LMM），基于1.4亿多患者索赔记录的事件序列训练，在成本预测和慢性病预测上分别优于最佳商业模型和Transformer模型，为医疗分析提供新工具。
摘要：
随着美国医疗保健支出接近5万亿美元（2024年国家医疗支出概况），且据估计其中25%属于浪费性支出（美国医疗保健系统中的浪费：估计成本与节约潜力，未注明日期），更好地预测风险和优化患者护理的需求愈发重要。本文介绍了大型医疗模型（LMM），这是一种生成式预训练Transformer（GPT），旨在指导和预测患者护理及医疗管理的广泛方面。该模型基于来自1.4亿多份纵向患者索赔记录的医疗事件序列进行训练，其专用词汇表构建自医疗术语系统，展现出预测医疗成本和识别潜在风险因素的卓越能力。通过实验与验证，我们展示了LMM不仅在成本和风险预测方面表现出色，还能识别复杂医疗状况中的复杂模式，并发现患者护理中的新关系。在预测广泛疾病方面，LMM的成本预测比最佳商业模型提升14.1%，慢性病预测比研究中的最佳Transformer模型提升1.9%。LMM是医疗分析领域的重大进步，有望显著增强风险评估、成本管理和个性化医疗。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
