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首页> 外文期刊>BMC Medical Informatics and Decision Making >A decision support system to follow up and diagnose primary headache patients using semantically enriched data
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A decision support system to follow up and diagnose primary headache patients using semantically enriched data

机译：一个决策支持系统，用于使用语义丰富的数据跟踪和诊断原发性头痛患者

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Headache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models are a black-box, deteriorating interpretability and transparency, which are key factors in order to be deployed in a clinical setting. In this paper, a decision support system is proposed, composed of three components: (i) a cross-platform mobile application to capture the required data from patients to formulate a diagnosis, (ii) an automated diagnosis support module that generates an interpretable decision tree, based on data semantically annotated with expert knowledge, in order to support physicians in formulating the correct diagnosis and (iii) a web application such that the physician can efficiently interpret captured data and learned insights by means of visualizations. We show that decision tree induction techniques achieve competitive accuracy rates, compared to other black- and white-box techniques, on a publicly available dataset, referred to as migbase. Migbase contains aggregated information of headache attacks from 849 patients. Each sample is labeled with one of three possible primary headache disorders. We demonstrate that we are able to reduce the classification error, statistically significant (ρ≤0.05), with more than 10% by balancing the dataset using prior expert knowledge. Furthermore, we achieve high accuracy rates by using features extracted using the Weisfeiler-Lehman kernel, which is completely unsupervised. This makes it an ideal approach to solve a potential cold start problem. Decision trees are the perfect candidate for the automated diagnosis support module. They achieve predictive performances competitive to other techniques on the migbase dataset and are, foremost, completely interpretable. Moreover, the incorporation of prior knowledge increases both predictive performance as well as transparency of the resulting predictive model on the studied dataset.
机译：头痛症是重要的健康负担，在全球范围内对健康和经济产生重大影响。当前的治疗和后续流程通常是过时的，这为计算机辅助和决策支持系统创造了提高效率的机会。现有系统大部分是完全由数据驱动的，其基础模型是一个黑匣子，使可解释性和透明性恶化，这是要在临床环境中部署的关键因素。本文提出了一种决策支持系统，该系统由三个组件组成：（i）跨平台移动应用程序，可从患者那里获取所需数据以制定诊断;（ii）生成可解释决策的自动诊断支持模块基于语义上用专业知识进行语义标注的数据树，以支持医师制定正确的诊断以及（iii）Web应用程序，以便医师可以通过可视化有效地解释捕获的数据和所学见识。我们显示，在称为migbase的公共数据集上，与其他黑盒和白盒技术相比，决策树归纳技术可实现竞争性的准确率。 Migbase包含来自849位患者的头痛发作的汇总信息。每个样品都标记有三种可能的原发性头痛疾病之一。我们证明，通过使用先验专家的知识来平衡数据集，我们能够减少10％以上的统计上显着的分类误差（ρ≤0.05）。此外，我们通过使用完全不受监督的Weisfeiler-Lehman内核提取的特征来获得较高的准确率。这使其成为解决潜在的冷启动问题的理想方法。决策树是自动化诊断支持模块的理想选择。与migbase数据集上的其他技术相比，它们可以实现预测性能，并且最重要的是，它们可以完全解释。此外，合并先验知识可提高预测性能以及所研究数据集上所得预测模型的透明度。

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