基于多模式表示和视觉语言模型的基于代码的医疗保险索赔的糖尿病预测
Wei Yang1, Hiromasa Yoshimoto2, Naohiro Mitsutake2
1The University of Tokyo, Japan.
Studies in health technology and informatics
|August 8, 2025
概括
使用医疗保险索赔 (MIC) 预测未来的医疗需求对于资源分配至关重要. 这项研究引入了一种新的多式联络方法,使用大型语言模型 (LLM) 来提高慢性疾病需求预测的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 公共卫生 公共卫生
背景情况:
- 与生活方式相关的慢性疾病 (如糖尿病和高脂血症) 的流行率不断上升,使全球的医疗保健系统面临压力.
- 准确预测未来的医疗需求对于有效的资源配置和规划至关重要.
- 公共医疗保险索赔 (MIC) 为健康趋势分析提供了有价值的,大规模的数据来源.
研究的目的:
- 使用日本医疗保险索赔 (MIC) 开发未来医疗需求的预测模型.
- 通过利用大语言模型 (LLM) 来实现半结构化,基于代码的保险数据来提高需求预测的准确性.
- 解决传统的LLM应用程序对复杂的医疗索赔数据的局限性.
主要方法:
- 提出了一种针对基于代码的医疗保险索赔 (MIC) 量身定制的多式联络代表技术.
- 开发并应用视觉语言模型 (VLM) 来预测未来的医疗需求.
- 利用来自日本全民医疗保健系统的公共医疗保险索赔的大数据集.
主要成果:
- 与基线方法相比,拟议的多式VLM显著提高了预测准确性.
- 在预测糖尿病相关病例的准确度上提高了3.8个点.
- 证明了复杂,半结构化医疗数据的多模式方法的有效性.
结论:
- 多式VLM方法为准确,数据驱动的医疗需求预测提供了一个有希望的解决方案.
- 这种方法可以帮助决策者和医疗保健提供者积极主动地管理和规划资源.
- 利用专业数据表示的LLM提高了对公共卫生挑战的预测能力.
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