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Alternative screening method for potential airborne disease---using Severe Acute Respiratory Syndrome data as an example.

机译：潜在的空气传播疾病的替代筛查方法-以严重急性呼吸系统综合症数据为例。

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摘要

Introduction: Infectious diseases are alarming. They are contagious and detrimental as the illnesses concern can transmit swiftly from one person to another within seconds. The potential wide spread of infections can cause chaotic situations when traveling abroad is so common and frequent nowadays. The disastrous incident of Severe Acute Respiratory Syndrome ("SARS" thereafter) in 2003, caused more than eight thousands people worldwide infected with an initial of only ten infected people who once stayed in the same hotel, is the best illustration of how powerful a deadly infection can be. Historical incidents have already proved that prompt detections and comprehensive surveillances are vital to the eradication of infectious diseases, but the most appropriate way for effective and efficient screening has not yet been confirmed as each of them carries its own advantages and disadvantages. In order to ensure the isolation precaution starts at the right time for effective containment, a sensitive and reliable screening approach to trigger off the whole process is crucial.;Purpose: To explore the feasibility of using data mining as an effective screening method to predict the occurrence of airborne disease based on the pre-hospitalized clinical presentations using the data of SARS in Hong Kong as an example.;Method: This study is an observational retrospective case record review study. All patients aged 18 or above, attending the Accident & Emergency Department (AED) of a major hospital during the period from 1st February 2003 to 30th June 2003 with provisional diagnosis of SARS, were recruited. Data collected for analysis included patient particulars, clinical presentations, co-morbidities, and laboratory results for confirmation of SARS based on the World Health Organisation (WHO) guidelines. There are four stages in this study. The first stage is the preparation of a comprehensive database for further analysis, followed by an evaluation of the existing prediction rules reported by others in stage two. The third stage is the attribute identification stage and the last stage is the model testing.;Results: A total of 549 adult case records were examined. Eighty percents of them were randomly selected to form the training dataset and the remaining cases were used as testing dataset. The testing data was fitted into the existing prediction model reported by Chen et al. (2004), Wang et al. (2004) and Leung et al. (2004) that all the studies were carried out in the most similar situation or inclusion criteria as the current study. The testing data was then classified into SARS and non-SARS based on each prediction rule and counterchecked with the laboratory diagnostic results. The sensitivity and specificity of each prediction rule were calculated and compared with the quoted value. The poor agreement of the calculated sensitivity (ranged from 0.17 to 0.95) and the specificity (ranged from 0 to 0.67) with the quoted values showed a strong need to have a new prediction model with better prediction power. Data mining technique was employed to see if it can be an alternative prediction method for airborne disease. Association rule mining could not find any sequential/affinity relationship between the clinical variables and the disease status. Classification rule mining showed that malaise, sore throat, fever and shortness of breath were critical clinical predictors where clustering method identified chills, malaise, sore throat and shortness of breath as critical clinical predictors. The testing data was fitted into the mined rules again and another set of calculated sensitivity (0.86) and specificity (0.71) values were obtained for comparison. The results were further tested under different circumstances and similar findings were obtained.;Conclusion: Data mining can be a better and an efficient option with higher specificity and sensitivity for predicting airborne disease in AED in the future.
机译：简介：传染病令人震惊。它们具有传染性和有害性，因为与疾病有关的疾病可以在几秒钟内迅速从一个人传播到另一个人。当如今出国旅行如此普遍和频繁时，潜在的广泛传播感染会导致混乱情况。 2003年发生的严重急性呼吸系统综合症（“ SARS”）灾难性事件导致全世界八千多人感染，最初只有十名感染者的感染者曾经住在同一家旅馆，这充分说明了致命人员的强大能力感染即可。历史事件已经证明，及时发现和进行全面监视对于根除传染病至关重要，但是尚未确认最有效和高效筛查的最佳方法，因为每种方法各有优缺点。为了确保隔离预防措施在正确的时间开始有效地遏制，触发整个过程的灵敏可靠的筛选方法至关重要。目的：探讨使用数据挖掘作为有效筛选方法来预测污染的可行性。方法：本研究是一项回顾性病例回顾性研究，以住院前的临床表现为基础，以SARS数据在香港为例。招募了所有在2003年2月1日至2003年6月30日期间就诊为SARS的大医院急诊科（AED），年龄在18岁以上。根据世界卫生组织（WHO）指南收集的用于分析的数据包括患者详情，临床表现，合并症​​和实验室结果，以确认SARS。本研究分为四个阶段。第一阶段是准备进一步分析的综合数据库，然后评估第二阶段其他人报告的现有预测规则。第三阶段是属性识别阶段，最后阶段是模型测试。结果：共检查了549例成人病例记录。随机选择其中的百分之八十以形成训练数据集，其余案例用作测试数据集。将测试数据拟合到Chen等人报告的现有预测模型中。 （2004），Wang等。 （2004年）和梁等人。 （2004年），所有研究都是在与当前研究最相似的情况或纳入标准下进行的。然后，根据每个预测规则将测试数据分为SARS和非SARS，并与实验室诊断结果进行核对。计算每个预测规则的敏感性和特异性，并将其与引用值进行比较。计算出的灵敏度（范围从0.17到0.95）和特异性（范围从0到0.67）与引用的值之间的一致性差，这表明强烈需要具有更好预测能力的新预测模型。使用数据挖掘技术来查看它是否可以作为空气传播疾病的替代预测方法。关联规则挖掘找不到临床变量与疾病状态之间的任何顺序/亲和关系。分类规则挖掘显示不适，喉咙痛，发烧和呼吸急促是关键的临床预测指标，而聚类方法将寒战，不适，喉咙痛和呼吸急促确定为关键的临床预测指标。将测试数据再次拟合到挖掘的规则中，并获得另一组计算出的灵敏度（0.86）和特异性（0.71）值以进行比较。在不同情况下进一步测试了结果，并获得了相似的发现。；结论：数据挖掘可以作为一种更好，更有效的选择，具有更高的特异性和敏感性，可用于将来预测AED中的空气传播疾病。

著录项

  • 作者

    Tai, Ling Yin Winnie.;

  • 作者单位

    Hong Kong Polytechnic University (Hong Kong).;

  • 授予单位 Hong Kong Polytechnic University (Hong Kong).;
  • 学科 Health sciences.;Pathology.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 242 p.
  • 总页数 242
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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