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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• 作者：Arjun Sharma ; Jason Hostetter ; James Morrison ; Kenneth Wang…
• 关键词：Decision Support ; Data mining ; Decision support techniques ; Web technology
• 刊名：Journal of Digital Imaging
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：29
• 期：2
• 页码：160-164
• 全文大小：501 KB
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• 作者单位：Arjun Sharma (1)
  Jason Hostetter (2)
  James Morrison (3)
  Kenneth Wang (4)
  Eliot Siegel (4)
  
  1. Department of Radiology, University of Maryland Medical Center, 22 S. Greene St, Baltimore, MD, 21201, USA
  2. University of Maryland Medical Center, Baltimore, MD, USA
  3. Oregon Hospital and Science University, Portland, OR, USA
  4. University of Maryland Medical Center, Baltimore VA Medical Center, Baltimore, MD, USA
  
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Imaging and Radiology
  
• 出版者：Springer New York
• ISSN：1618-727X

文摘

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.