Analyzing Medical Data with Process Mining: a COVID-19 Case Study

使用Apriori关联规则挖掘从康复和死亡患者中发现COVID-19的症状模式

Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining

本文利用Apriori关联规则挖掘算法分析2875名COVID-19患者数据，识别出呼吸暂停、咳嗽、发热等主要症状模式，为临床治疗提供决策支持。

Process Mining-Driven Analysis of the COVID19 Impact on the Vaccinations of Victorian Patients

Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining tools in different contexts, including healthcare. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we report the analysis of an event log recording more …

An Approach to Find Missing Values in Medical Datasets

Mining medical datasets is a challenging problem before data mining researchers as these datasets have several hidden challenges compared to conventional datasets.Starting from the collection of samples through field experiments and clinical trials to performing classification,there are numerous challenges at every stage in the mining process. The preprocessing phase in the mining process itself is a challenging issue when, we work on medical datasets. One of the prime challenges in mining medical datasets is handling missing values which is part of preprocessing phase. In this paper, we addre…

A Novel Approach for Imputation of Missing Attribute Values for Efficient Mining of Medical Datasets - Class Based Cluster Approach

Missing attribute values are quite common in the datasets available in the literature. Missing values are also possible because all attributes values may not be recorded and hence unavailable due to several practical reasons. For all these one must fix missing attribute vales if the analysis has to be done. Imputation is the first step in analyzing medical datasets. Hence this has achieved significant contribution from several medical domain researchers. Several data mining researchers have proposed various methods and approaches to impute missing values. However very few of them concentrate o…

基于Fastmap算法的医学信息数据库可视化数据采掘

Visual approach for data mining on medical information databases using Fastmap algorithm

信息获取和存储工具的快速发展导致了巨大的医学数据库的形成。大量的数据绝对超过了人类在没有专门分析工具的情况下有效使用的能力。这种情况被描述为数据丰富，但信息贫乏。为了填补这一日益增长的差距，不同的方法从数据采掘领域的应用。这些方法对大量观察到的数据进行分析，以便找到新的依赖关系或数据的简洁表示，这对人类更有意义。用于发现依赖关系的可能方法之一是可视化方法，在该方法中，数据以适合于由领域专家分析的方式被处理和可视化。这项工作提出了一种可视化的方法，在该方法中，数据被处理和可视化的方式适合于分析领域的专家。我们设计并实现了一个软件解决方案的多维可视化，分类的医疗数据使用FastMap算法的毕业生降维。图形用户界面的实现进行了详细描述，因为它是最重要的因素，这些工具的易用性，由非专业人士在数据采掘。

应用数据采掘方法远程跟踪帕金森病进展的比较研究

A Comparative Study on Remote Tracking of Parkinsons Disease Progression Using Data Mining Methods

近年来，数据采掘方法在医学诊断和评估的许多领域中的应用越来越普遍。数据采掘方法是发现和提取医学数据库中可用知识的合适工具。本研究将11种数据采掘算法分为5组，应用于帕金森病（PD）患者临床变量数据集，研究疾病进展。该数据集包括42个人的22个属性，我们所有的算法都应用于这个数据集。相关系数为0.9985的决策表具有最好的精度，相关系数为0.7919的决策树桩具有最低的精度。