Abstract: In medical practice, the collection of physiologic indicators from patients is often key to diagnosing disease. However, in reality, patients' physiological data are often uncertain and ambiguous. Belief rule base BRB (BRB) is an expert system approach that efficiently handles a variety of uncertainty and ambiguity information by combining expert knowledge to transform data into confidence distributions. However, current BRB-based disease diagnosis models still rely on offline training methods, which is insufficient to meet the dynamic real-time requirements in disease diagnosis environments. In addition, existing online models in other fields suffer from the problem of exploding number of training data samples and sample imbalance. Therefore, in this paper, we propose an online belief rule base disease diagnosis method based on the human-in-the-loop strategy. First, the traditional offline training BRB disease diagnosis model is improved into an online training model, so that the model can realize dynamic growth according to different patients' physiological indicators. Second, a human-in-the-loop algorithm is proposed in the online learning BRB model to enhance the decision-making ability of experts. The problems of explosive growth of training samples, overfitting of model output and sample imbalance in traditional online models are effectively solved. Finally, the effectiveness and superiority of the method is verified through experiments on chronic kidney disease grading, hepatitis C prediction, breast cancer diagnosis and diabetes diagnosis.

摘要： 在医疗实践中，收集病人的生理指标往往是诊断疾病的关键。然而，在现实中，病人的生理数据往往是不确定和模糊的。置信规则库（Belief rule base BRB）是一种专家系统方法，它通过结合专家知识将数据转化为置信分布，从而有效地处理各种不确定性和模糊性信息。然而，目前基于BRB的疾病诊断模型仍依赖于离线训练方法，这不足以满足疾病诊断环境中的动态实时要求。此外，其他领域现有的在线模型也存在训练数据样本数量爆炸式增长和样本不平衡的问题。因此，本文提出了一种基于人在回路策略的在线置信规则库疾病诊断方法。首先，将传统的离线训练BRB疾病诊断模型改进为在线训练模型，使模型能够根据不同患者的生理指标实现动态增长。其次，在在线学习的BRB模型中，提出了一种人在回路算法，以增强专家的决策能力。有效解决了传统在线模型中训练样本爆炸式增长、模型输出过拟合和样本不平衡等问题。最后，通过对慢性肾病分级、丙型肝炎预测、乳腺癌诊断和糖尿病诊断的实验，验证了该方法的有效性和优越性。