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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Using unsupervised learning to determine risk level for left ventricular diastolic dysfunction
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Using unsupervised learning to determine risk level for left ventricular diastolic dysfunction

机译：使用无监督学习来确定左心室舒张功能障碍的风险水平

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Left Ventricular Diastolic Dysfunction (LVDD) is a decompensatory change in the relaxation properties of the heart, the risk for which increases with age. Currently, physicians use a decision-tree-like algorithm to distinguish between discrete LVDD levels. This approach, based on cut-off thresholds, can potentially lead to information loss and possibly to misdiagnosis. This paper aims to explore an alternative diagnostic method to determine LVDD risk level, taking into account a wide variety of attributes available in patient records, without pre-setting cut-off thresholds. Using a large dataset derived from the Baltimore Longitude Study of Aging (BLSA), and adjusting the data for age and gender, we employ the Chi Square test and the information gain criterion to identify attributes that correlate well with the physician-assigned grades; such attributes are referred to as distinguishing attributes. We then apply the expectation maximization (EM) algorithm, as well as the K-Means, in order to cluster records that are represented using distinguishing attributes. While clusters resulting from the K-Means are not stable, three stable and tightly-formed clusters, which are obtained from the EM algorithm, roughly correspond to the physician-assigned categories. Based on the results from the EM algorithm, we can compute a patient's probability to have low, high or no risk for LVDD, and use this probability as a basis for defining a risk score to determine the patient's LVDD severity.
机译：左心室舒张功能障碍（LVDD）是心脏舒张特性的代偿性变化，其风险会随着年龄的增长而增加。当前，医生使用类似决策树的算法来区分离散的LVDD电平。这种基于临界阈值的方法可能会导致信息丢失并可能导致误诊。本文旨在探讨一种可确定LVDD风险水平的替代诊断方法，同时考虑到病历中可用的各种属性，而无需预先设置截止阈值。使用来自巴尔的摩的经度衰老研究（BLSA）的大型数据集，并调整年龄和性别的数据，我们采用卡方检验和信息获取标准来识别与医师分配的评分很好相关的属性;这样的属性称为区别属性。然后，我们应用期望最大化（EM）算法以及K均值，以便对使用区别属性表示的记录进行聚类。虽然由K均值得出的聚类不稳定，但从EM算法获得的三个稳定且紧密形成的聚类大致对应于医师分配的类别。根据EM算法的结果，我们可以计算出患者发生LVDD风险低，高或没有的概率，并以此概率为基础确定风险评分，以确定患者的LVDD严重程度。

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