在自动诊断系统中聚合多式联络组合模型的模型无学信心测量
1Department of Applied Artificial Intelligence Major in Bio Artificial Intelligence, Hanyang University, Ansan, 15588, Republic of Korea.
Methods (San Diego, Calif.)
|September 28, 2024
概括
自动诊断系统 (ADS) 协助临床医生,改善医疗保健的获取. 一个新的框架通过改进不同诊断模型如何进行诊断来增强多式联运组合.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 机器学习用于医疗保健
背景情况:
- 自动诊断系统 (ADS) 帮助临床医生,并扩大医疗保健的获取.
- 多模组合方法是有效的,但与聚合多样化的模型输出作斗争.
- 现有的方法不充分结合不同诊断标准的概率.
研究的目的:
- 开发一个改进的聚合框架,用于诊断系统中的多式联络组合.
- 解决来自异质诊断模型的概率组合的局限性.
- 提高自动疾病诊断的准确性和可靠性.
主要方法:
- 为多式联络乐队提出了一个新的聚合框架.
- 开发了一种方法来将概率得分转化为统一的,基于规则的得分.
- 实施了一个模型不可知的信心测量,使用后分析对异质预测.
主要成果:
- 拟议的框架有效地汇总了模型不可知的信心得分和预测.
- 基于统一聚合规则的分数准确地反映了模型的输出.
- 实验结果显示,与现有方法相比,性能优越.
结论:
- 新的框架为ADS中的多式联络组合聚合提供了一个强大的解决方案.
- 改进的聚合增强了个别模型优势的利用.
- 这种方法促进了更可靠,更准确的诊断系统的发展.
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