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首页> 美国卫生研究院文献>Journal of Digital Imaging >Focused Decision Support: a Data Mining Tool to Query the Prostate Lung Colorectal and Ovarian Cancer Screening Trial Dataset and Guide Screening Management for the Individual Patient
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Focused Decision Support: a Data Mining Tool to Query the Prostate Lung Colorectal and Ovarian Cancer Screening Trial Dataset and Guide Screening Management for the Individual Patient

机译：重点决策支持：一种数据挖掘工具可查询各个患者的前列腺肺结肠直肠和卵巢癌筛查试验数据集并指导筛查管理

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The Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial enrolled ~155,000 participants to determine whether certain screening exams reduced mortality from prostate, lung, colorectal, and ovarian cancer. Repurposing the data provides an unparalleled resource for matching patients with the outcomes of demographically or diagnostically comparable patients. A web-based application was developed to query this subset of patient information against a given patient’s demographics and risk factors. Analysis of the matched data yields outcome information which can then be used to guide management decisions and imaging software. Prognostic information is also estimated via the proportion of matched patients that progress to cancer. The US Preventative Services Task Force provides screening recommendations for cancers of the breast, colorectal tract, and lungs. There is wide variability in adherence of clinicians to these guidelines and others published by the Fleischner Society and various cancer organizations. Data mining the PLCO dataset for clinical decision support can optimize the use of limited healthcare resources, focusing screening on patients for whom the benefit to risk ratio is the greatest and most efficacious. A data driven, personalized approach to cancer screening maximizes the economic and clinical efficacy and enables early identification of patients in which the course of disease can be improved. Our dynamic decision support system utilizes a subset of the PLCO dataset as a reference model to determine imaging and testing appropriateness while offering prognostic information for various cancers.
机译：前列腺癌，肺癌，结直肠癌和卵巢癌（PLCO）筛查试验招募了约155,000名参与者，以确定某些筛查检查是​​否可以降低前列腺癌，肺癌，结直肠癌和卵巢癌的死亡率。重新利用数据可为匹配患者与人口统计学或诊断可比患者的结果提供无与伦比的资源。开发了一个基于Web的应用程序，以根据给定患者的人口统计信息和风险因素查询患者信息的此子集。对匹配数据的分析会产生结果信息，然后可将其用于指导管理决策和成像软件。还通过匹配的癌症患者的比例来估计预后信息。美国预防服务工作队提供了针对乳腺癌，结肠直肠癌和肺癌的筛查建议。临床医生遵守这些指南以及Fleischner Society和各种癌症组织出版的其他指南存在很大差异。对PLCO数据集进行数据挖掘以进行临床决策支持的数据可以优化有限医疗资源的使用，重点是筛查受益/风险比最大，最有效的患者。数据驱动的个性化癌症筛查方法可最大程度地提高经济和临床疗效，并能及早识别可改善病程的患者。我们的动态决策支持系统利用PLCO数据集的子集作为参考模型来确定成像和测试的适当性，同时提供各种癌症的预后信息。

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