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Visually guided classification trees for analyzing chronic patients.
BMC Bioinformatics ( IF 3 ) Pub Date : 2020-03-11 , DOI: 10.1186/s12859-020-3359-3
Cristina Soguero-Ruiz 1 , Inmaculada Mora-Jiménez 1 , Miguel A Mohedano-Munoz 2 , Manuel Rubio-Sanchez 2 , Pablo de Miguel-Bohoyo 3 , Alberto Sanchez 2, 4
Affiliation  

Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights. In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses. We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.

中文翻译:

视觉引导的分类树,用于分析慢性患者。

发达国家每年的慢性病正变得越来越普遍,这主要是由于预期寿命的延长。其中,糖尿病(DM)和原发性高血压(EH)是最流行的两种。此外,它们可能是其他慢性疾病的发作,例如肾脏或阻塞性肺疾病。理解与此类复杂疾病相关的因素的需求推动了解释性和视觉分析方法(例如分类树)的发展,分类树不仅为诊断患者提供了预测模型,而且还有助于发现新的临床见解。在本文中,我们分析了与西班牙丰拉夫拉达大学医院有关的健康和慢性(糖尿病,高血压)患者。根据临床风险组(CRG)将每位患者分类为单一健康状况。CRG通过年龄,性别,诊断代码和药物代码等特征来表征患者。基于这些特征和CRG,我们设计了分类树来确定不同健康状况之间最具区别性的决策特征。特别是,我们建议在构建树时利用统计数据可视化来指导每个节点中特征的选择。我们创建了一些分类树来区分具有不同健康状况的患者。我们根据分类准确性分析了它们的性能,并就每棵树中考虑的决策特征得出了临床结论。不出所料 健康患者和患有单一慢性病的患者比合并症患者的分类更好。所构建的分类树还显示,结合常规的DM和/或EH诊断,使用抗精神病药和诊断慢性气道阻塞与对一种以上慢性病的患者进行分类有关。我们提出了一种在视觉引导下构造分类树的方法。该方法允许临床医生逐步选择每个树节点上的决策特征。该过程由探索性数据分析可视化指导,可提供新的见解和意想不到的临床信息。所构建的分类树还显示,结合常规的DM和/或EH诊断,使用抗精神病药和诊断慢性气道阻塞与对一种以上慢性病的患者进行分类有关。我们提出了一种在视觉引导下构造分类树的方法。该方法允许临床医生逐步选择每个树节点上的决策特征。该过程由探索性数据分析可视化指导,可提供新的见解和意想不到的临床信息。构造的分类树还显示,结合常规的DM和/或EH诊断,抗精神病药的使用和慢性气道阻塞的诊断与多于一种慢性病的患者分类相关。我们提出了一种在视觉引导下构造分类树的方法。该方法允许临床医生逐步选择每个树节点上的决策特征。该过程由探索性数据分析可视化指导,可提供新的见解和意想不到的临床信息。该方法允许临床医生逐步选择每个树节点上的决策特征。该过程由探索性数据分析可视化指导,可提供新的见解和意想不到的临床信息。该方法允许临床医生逐步选择每个树节点上的决策特征。该过程由探索性数据分析可视化指导,可提供新的见解和意想不到的临床信息。
更新日期:2020-03-16
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