We propose a Bayesian approach for both medical inquiry and disease inference, the two major phases in differential diagnosis. Unlike previous work that simulates data from given probabilities and uses ML algorithms on them, we directly use the Quick Medical Reference (QMR) belief network, and apply Bayesian inference in the inference phase and Bayesian experimental design in the inquiry phase. Moreover, we improve the inquiry phase by extending the Bayesian experimental design framework from one-step search to multi-step search. Our approach has some practical advantages as it is interpretable, free of costly training, and able to adapt to new changes without any additional effort. Our experiments show that our approach achieves new state-of-the-art results on two simulated datasets, SymCAT and HPO, and competitive results on two diagnosis dialogue datasets, Muzhi and Dxy.
翻译:我们建议采用贝叶斯法进行医学调查和疾病推断,这是不同诊断的两个主要阶段。与以前模拟从特定概率中得出的数据并使用ML算法的工作不同,我们直接使用快速医疗参考(QMR)信仰网络,在推断阶段应用贝叶斯推断法,在调查阶段应用Bayesian实验设计。此外,我们通过将贝叶斯实验设计框架从一步搜索扩大到多步搜索,改进了调查阶段。我们的方法有一些实际优势,因为它可以解释,无需经过昂贵的培训,能够适应新的变化而无需再做任何努力。我们的实验表明,我们的方法在两个模拟数据集(SymCAT和HPO)上取得了新的最新结果,并在两个诊断对话数据集(Muzhi和Dxy)上取得了竞争性结果。