Welcome to the InfoSci Platform
Reference Hub9
Nonparametric Decision Support Systems in Medical Diagnosis: Modeling Pulmonary Embolism

Nonparametric Decision Support Systems in Medical Diagnosis: Modeling Pulmonary Embolism

Steven Walczak, Bradley B. Brimhall, Jerry B. Lefkowitz
Copyright: © 2006 |Volume: 1 |Issue: 2 |Pages: 18
ISSN: 1555-3396|EISSN: 1555-340X|ISSN: 1555-3396|EISBN13: 9781615203017|EISSN: 1555-340X|DOI: 10.4018/jhisi.2006040105
Cite Article Cite Article

MLA

Walczak, Steven, et al. "Nonparametric Decision Support Systems in Medical Diagnosis: Modeling Pulmonary Embolism." IJHISI vol.1, no.2 2006: pp.65-82. http://doi.org/10.4018/jhisi.2006040105

APA

Walczak, S., Brimhall, B. B., & Lefkowitz, J. B. (2006). Nonparametric Decision Support Systems in Medical Diagnosis: Modeling Pulmonary Embolism. International Journal of Healthcare Information Systems and Informatics (IJHISI), 1(2), 65-82. http://doi.org/10.4018/jhisi.2006040105

Chicago

Walczak, Steven, Bradley B. Brimhall, and Jerry B. Lefkowitz. "Nonparametric Decision Support Systems in Medical Diagnosis: Modeling Pulmonary Embolism," International Journal of Healthcare Information Systems and Informatics (IJHISI) 1, no.2: 65-82. http://doi.org/10.4018/jhisi.2006040105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Patients face a multitude of diseases, trauma, and related medical problems that are difficult to diagnose and have large treatment and diagnostic direct costs, including pulmonary embolism (PE), which has mortality rates as high as 10%. Advanced decision-making tools, such as nonparametric neural networks (NN), may improve diagnostic capabilities for these problematic medical conditions. The research develops a backpropagation trained neural network diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for PE, with almost 15% suffering a confirmed PE, were collected and used to evaluate various NN models’ performances. Results indicate that using NN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, specifically the d-dimer assay and reactive glucose, significantly improving overall positive predictive value, compared to using either test in isolation, and also increasing negative predictive performance.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.