Description

Book Synopsis
For the development of clinical decision support systems based on Bayesian networks, Mario A. Cypko investigates comprehensive expert models of multidisciplinary clinical treatment decisions and solves challenges in their modeling. The presented methods, models and tools are developed in close and intensive cooperation between knowledge engineers and clinicians. In the course of this study, laryngeal cancer serves as an exemplary treatment decision. The reader is guided through a development process and new opportunities for research and development are opened up: in modeling and validation of workflows, guided modeling, semi-automated modeling, advanced Bayesian networks, model-user interaction, inter-institutional modeling and quality management.

Table of Contents
Patient-specific Bayesian Network in a Clinical Environment.- TreLynCa: A Tumor Board Decision Model for Laryngeal Cancer.- Model Validation and Tools for Guided BN Modeling.- GUI for PSBN-based decision verification.

Development of Clinical Decision Support Systems using Bayesian Networks: With an example of a Multi-Disciplinary Treatment Decision for Laryngeal Cancer

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A Paperback by Mario A. Cypko

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    View other formats and editions of Development of Clinical Decision Support Systems using Bayesian Networks: With an example of a Multi-Disciplinary Treatment Decision for Laryngeal Cancer by Mario A. Cypko

    Publisher: Springer Fachmedien Wiesbaden
    Publication Date: 01/12/2020
    ISBN13: 9783658325930, 978-3658325930
    ISBN10: 3658325933
    Also in:
    Applied computing Artificial intelligence (AI) Expert systems / knowledge-based systems

    Description

    Book Synopsis
    For the development of clinical decision support systems based on Bayesian networks, Mario A. Cypko investigates comprehensive expert models of multidisciplinary clinical treatment decisions and solves challenges in their modeling. The presented methods, models and tools are developed in close and intensive cooperation between knowledge engineers and clinicians. In the course of this study, laryngeal cancer serves as an exemplary treatment decision. The reader is guided through a development process and new opportunities for research and development are opened up: in modeling and validation of workflows, guided modeling, semi-automated modeling, advanced Bayesian networks, model-user interaction, inter-institutional modeling and quality management.

    Table of Contents
    Patient-specific Bayesian Network in a Clinical Environment.- TreLynCa: A Tumor Board Decision Model for Laryngeal Cancer.- Model Validation and Tools for Guided BN Modeling.- GUI for PSBN-based decision verification.

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