一种混合强化学习和知识图框架,用于医疗保健系统的财务风险优化
Md Shahab Uddin1, Ahsan Ahmed2, Md Aktarujjaman3
1Department of Computer Science, Maharishi International University, Fairfield, IA, 52557, USA.
Scientific reports
|August 9, 2025
概括
本研究介绍了一种混合人工智能框架,使用强化学习和知识图来优化医疗保健计费,提高成本效益,同时保持诊断准确性. 这种新的方法提高了复杂的医疗保健系统的财务风险管理.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 计算经济学 计算经济学
背景情况:
- 在医疗保健中有效的财务风险管理需要平衡治疗质量和成本效益.
- 智能决策对于优化医疗保健系统的资源配置至关重要.
- 当前的方法往往难以将财务优化与临床有效性相结合.
研究的目的:
- 提出一种新的混合框架,将强化学习 (RL) 与知识图增强的神经网络相结合.
- 优化医疗保健计费决策,同时保持诊断准确度.
- 加强医疗保健系统的财务风险管理和成本效益.
主要方法:
- 使用结构化特征,深度潜伏表示和知识图语义嵌入编码的患者个人资料.
- 使用深度Q网络 (DQN) 或近接政策优化 (PPO) 进行训练的强化学习代理.
- 混合框架将数据驱动的学习与结构化的医疗知识相结合,以实现情境意识的决策.
主要成果:
- 拟议的模型显著优于传统的回归器,深度神经网络和独立的RL代理.
- 在成本预测错误,诊断分类准确性和累积奖励方面取得了明显的改进.
- 在保持临床有效性的同时,实现了显著的平均计费减少.
结论:
- 将数据驱动学习与结构化医学知识相结合,对于医疗保健决策有价值.
- 混合框架使复杂的医疗保健环境中能够实现环境意识和成本效益的财务管理.
- 这种方法为优化计费策略和提高医疗保健系统整体效率提供了一个有希望的解决方案.
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