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价格:为罕见疾病诊断提供个性化的递归智能成本估计框架

Mengshu Nie¹, Yujing Yao², Junyoung Kim¹

¹Boston Children's Hospital, Department of Pediatrics, Division of Genetics and Genomics, Boston, MA.

Research square

|June 30, 2025

概括

PRICE框架提供了一种动态的方法来评估罕见疾病的诊断策略的成本效益. 这种与人工智能集成的模型可以个性化评估,改善临床医生和患者的决策.

科学领域:

医疗信息学医疗信息学
卫生经济学卫生经济学
人工智能在医学中的应用

背景情况:

诊断罕见疾病是复杂的,昂贵的,耗时的.
传统的成本效益分析使用静态模型,无法考虑患者的变化.
需要动态,个性化的框架来实现诊断成本效益,特别是随着人工智能的进步.

研究的目的:

引入PRICE (个性化递归综合性成本效益) 框架.
开发一个动态模型来评估诊断策略的成本效益在个体患者的水平.
适应专家驱动和人工智能辅助的诊断决策.

主要方法:

使用基于树的分析模型 (PRICE) 与反向传播算法进行成本计算.
使用基于实用性的方法量化有效性.
纳入诸如疾病流行率,测试成本,绩效指标和周转时间等参数,以进行个性化评估.

主要成果:

应用PRICE对发育迟缓和多种先天性异常的诊断策略.
证明了PRICE在不同参数下建模结果的能力,支持患者的决策.
展示了AI性能如何影响AI授权场景中的最佳成本效益策略.
开发了一种交互式网络工具,用于实时可视化和模拟诊断途径.

结论:

价格是一个灵活的框架,可以捕捉连续的诊断工作流,适应AI集成.
能够对诊断策略进行个性化的经济和临床评估.
促进知情,个性化的决策,尤其对于罕见疾病诊断至关重要.

关键词:

人工智能授权模式对成本效益的分析.诊断工作流的建模工作流.

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