Abstract：
Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the \"gatekeepers\" of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems.
In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n-way, k-shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n×k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset.
Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90.02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29.13 % and 21.63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results.
The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.
In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n-way, k-shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n×k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset.
Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90.02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29.13 % and 21.63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results.
The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.
摘要：
背景：诊断错误已成为初级卫生保健中对患者安全的最大威胁。全科医生,作为初级卫生保健的“看门人”，有责任准确诊断患者。然而,许多全科医生对某些疾病的知识和临床经验不足。需要开发临床决策工具，以有效改善初级卫生保健的诊断过程。医学数据集的长尾类分布对许多基于深度学习的流行决策模型具有挑战性，很难预测很少的疾病。元学习是解决少数问题的一种新策略。
方法：在本研究中，提出了一种基于模型不可知元学习算法(FSDD-MAML)的少量疾病诊断决策模型.将MAML算法应用于基于知识图的疾病诊断模型中,寻找最优模型参数。此外，FSDD-MAML可以学习基于知识图的疾病诊断模型的所有模块的学习率。对于n-way，k-shot学习任务，FSDD-MAML的内部循环执行多个梯度更新步骤，以使用n×k个示例来学习疾病分类任务中的内部特征，FSDD-MAML的外循环优化了元目标，以找到相关的最佳参数和学习率。将FSDD-MAML与基于腹部疾病数据集的基于原始知识图的疾病诊断模型和其他元学习算法进行比较。
结果：与前3，前5和前10评估相比，元学习算法可以大大提高模型在前1评估中的性能。拟议的决策模型FSDD-MAML优于所有其他模型，精度@1为90.02%。我们在所有疾病的诊断中实现了最先进的表现，对少数疾病的预测性能大大提高。对于疾病例子最少的两组，与原始基于知识图的疾病诊断模型相比，FSDD-MAML在精度@1方面实现了29.13%和21.63%的相对提高。此外,我们分析了几种少数疾病预测的推理过程，并为结果提供了解释。
结论：本文提出的基于元学习的决策模型能够支持全科医生对疾病的快速诊断，尤其能够帮助全科医生诊断少发疾病。本研究对元学习在全科少发疾病评估中的探索和应用具有深远的意义。
方法：在本研究中，提出了一种基于模型不可知元学习算法(FSDD-MAML)的少量疾病诊断决策模型.将MAML算法应用于基于知识图的疾病诊断模型中,寻找最优模型参数。此外，FSDD-MAML可以学习基于知识图的疾病诊断模型的所有模块的学习率。对于n-way，k-shot学习任务，FSDD-MAML的内部循环执行多个梯度更新步骤，以使用n×k个示例来学习疾病分类任务中的内部特征，FSDD-MAML的外循环优化了元目标，以找到相关的最佳参数和学习率。将FSDD-MAML与基于腹部疾病数据集的基于原始知识图的疾病诊断模型和其他元学习算法进行比较。
结果：与前3，前5和前10评估相比，元学习算法可以大大提高模型在前1评估中的性能。拟议的决策模型FSDD-MAML优于所有其他模型，精度@1为90.02%。我们在所有疾病的诊断中实现了最先进的表现，对少数疾病的预测性能大大提高。对于疾病例子最少的两组，与原始基于知识图的疾病诊断模型相比，FSDD-MAML在精度@1方面实现了29.13%和21.63%的相对提高。此外,我们分析了几种少数疾病预测的推理过程，并为结果提供了解释。
结论：本文提出的基于元学习的决策模型能够支持全科医生对疾病的快速诊断，尤其能够帮助全科医生诊断少发疾病。本研究对元学习在全科少发疾病评估中的探索和应用具有深远的意义。
