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Entropy-Driven Decision Tree Building for Decision Support in Gastroenterology

机译：熵驱动的决策树在胃肠病学中的决策支持

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Gastroesophageal reflux disease is a serious clinical problem, which can significantly impair health-related quality of life, thus having global implications for patients. The first step for a doctor is the clinical classification of the patients, divided into classes after being subjected to endoscopic examinations to control if there are lesions of the esophageal mucosa, and if present, the severity of these lesions. 269 patients were taken into consideration (4 healthy patients, 219 with non erosive reflux disease, 21 with erosive reflux disease, 15 with complicated erosive reflux disease, 10 with Barrett's disease). A set of values taken from gastroscopy, ph-metry and manometry tests were considered and a decision tree was made to classify every patient. Entropy and information gain were calculated for each node to create the most possible simple tree. The resulting tree presents some paths including a significant number of persons; the values that build these paths can be considered characteristic of each class of patient. This method can be a basis to develop a diagnostic decision support for a training doctor starting from a set of characteristics, specific to a class of patient.
机译：胃食管反流疾病是一个严重的临床问题，可能严重损害健康相关的生活质量，因此对患者具有全球性影响。医生的第一步是对患者进行临床分类，在进行内窥镜检查后将其分为几类，以控制食管粘膜是否存在病变，以及是否存在这些病变的严重程度。考虑到269例患者（4例健康患者，219例患有非糜烂性反流病，21例患有糜烂性反流病，15例患有复杂性糜烂性反流病，10例患有Barrett病）。考虑了从胃镜检查，ph值测定法和测压法测试获得的一组值，并制作了决策树以对每个患者进行分类。为每个节点计算熵和信息增益，以创建最可能的简单树。生成的树呈现出一些路径，其中包括大量人员。建立这些路径的价值可被视为每一类患者的特征。此方法可以作为从特定于一类患者的一组特征开始为培训医生开发诊断决策支持的基础。

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