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Clinical decision modeling system

Haiwen Shi1 and James Lyons-Weiler123456*

  • * Corresponding author: James Lyons-Weiler jfl2@pitt.edu

  • † Equal contributors

Author affiliations

1 Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories, 3343 Forbes Avenue, Pittsburgh, PA 15260 USA

2 Department of Biomedical Informatics, University of Pittsburgh Medical School and University of Pittsburgh Graduate School of Public Health, Parkvale Building M-183, 200 Meyran Avenue, Pittsburgh, PA 15260 USA

3 Department of Pathology, University of Pittsburgh, School of Medicine, S-417 BST, 200 Lothrop Street, Pittsburgh, PA 15261 USA

4 Clinical Genomics Facility and Clinical Proteomics Facility, University of Pittsburgh Cancer Institute, Hillman Cancer Center, UPCI Research Pavilion, Suite 2.26d, 5177 Centre Ave., Pittsburgh, PA 15213-1863, USA

5 Interdisciplinary Biomedical Graduate Program, University of Pittsburgh, School of Medicine Graduate Office, 524 Scaife Hall, Pittsburgh, PA 15261-0001 USA

6 University of Pittsburgh Cancer Institute, 5150 Centre Ave, Pittsburgh, PA 15232, USA

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Citation and License

BMC Medical Informatics and Decision Making 2007, 7:23  doi:10.1186/1472-6947-7-23

Published: 13 August 2007

Abstract

Background

Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified.

Methods

We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer.

Results

Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to estimate clinical impact. High-performance combinations of clinical options may exist for breast and lung cancer detection.

Conclusion

The software could be found useful in simplifying the objective-driven planning of complex integrative clinical studies without requiring a multi-attribute utility function, and it could lead to efficient integrative translational clinical study designs that move beyond simple pair wise competitive studies. Collaborators, who traditionally might compete to prioritize their own individual clinical options, can use the software as a common framework and guide to work together to produce increased understanding on the benefits of using alternative clinical combinations to affect strategic and cost-effective clinical workflows.