Tremors are one of the most common movement disorders primarily associated with various neurological diseases. Since there are more than 20 different types of tremors, differentiation between them is important from the treatment point of view. In the thesis, we focus on differentiation between three of the most common tremors: Parkinsonian, essential and mixed type of tremor. Our first goal was to build a diagnostic model for distinguishing between Parkinsonian, essential and mixed type of tremors, based on clinical examination data, family history and digital spirography. The process of building a model was carried out using argument-based machine learning which enabled us to build a decision model through the process of knowledge elicitation from the domain expert (in our case from a neurologist). The obtained model consists of thirteen rules that are medically sensible. The process of knowledge elicitation itself contributed to the higher classification accuracy of the final model in comparison with the initial one. In the final diagnostic model, attributes derived from the spirography were included in more than half of the rules. This motivated us to build a model based solely on the digital spirography data. For the needs of constructing an understandable model, we first built several attributes which represented domain medical knowledge. We have built more than 500 different attributes which were used in a logistic regression to construct the final diagnostic model. The model is able to distinguish subjects with tremors from those without tremors with 90% classification accuracy. During the process of attribute construction, we wanted to know what our attributes were detecting. Thus, we have developed a method for attribute visualisation on series. The method not only helped us with attribute construction, but it is also useful for visual interpretation of the diagnostic model's decisions. The visualisation method and consequently the decision model were evaluated with the help of three independent neurology experts. The results show that both the diagnostic model and the visualisation are meaningful and cover medical knowledge of the domain. The final diagnostic model is built into the freely available ParkinsonCheck mobile application.

中文翻译：

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用于模拟震颤机制的人工智能方法

震颤是最常见的运动障碍之一，主要与各种神经系统疾病有关。由于有 20 多种不同类型的震颤，从治疗的角度来看，区分它们很重要。在本论文中，我们重点关注三种最常见震颤之间的区别：帕金森、特发性和混合型震颤。我们的第一个目标是建立一个诊断模型，根据临床检查数据、家族史和数字肺功能图来区分帕金森病、特发性和混合型震颤。构建模型的过程是使用基于参数的机器学习进行的，这使我们能够通过领域专家（在我们的案例中来自神经病学家）的知识获取过程来构建决策模型。获得的模型由十三个医学上合理的规则组成。与初始模型相比，知识获取过程本身有助于最终模型的分类准确率更高。在最终的诊断模型中，一半以上的规则中包含了源自肺活量图的属性。这促使我们建立一个仅基于数字肺活量数据的模型。为了构建可理解模型的需要，我们首先构建了几个代表领域医学知识的属性。我们已经构建了 500 多个不同的属性，这些属性用于逻辑回归以构建最终的诊断模型。该模型能够以 90% 的分类准确率区分有震颤的受试者和没有震颤的受试者。在属性构建过程中，我们想知道我们的属性检测到了什么。因此，我们开发了一种用于系列属性可视化的方法。该方法不仅帮助我们构建属性，而且对于诊断模型决策的可视化解释也很有用。在三位独立的神经病学专家的帮助下，对可视化方法以及决策模型进行了评估。结果表明，诊断模型和可视化都是有意义的，并且涵盖了该领域的医学知识。最终诊断模型内置于免费提供的 ParkinsonCheck 移动应用程序中。但它对于诊断模型决策的可视化解释也很有用。在三位独立的神经病学专家的帮助下，对可视化方法以及决策模型进行了评估。结果表明，诊断模型和可视化都是有意义的，并且涵盖了该领域的医学知识。最终诊断模型内置于免费提供的 ParkinsonCheck 移动应用程序中。但它对于诊断模型决策的可视化解释也很有用。在三位独立的神经病学专家的帮助下，对可视化方法以及决策模型进行了评估。结果表明，诊断模型和可视化都是有意义的，并且涵盖了该领域的医学知识。最终诊断模型内置于免费提供的 ParkinsonCheck 移动应用程序中。