| Description |
1 online resource (xiv, 148 pages) : illustrations |
| Contents |
Intro -- Acknowledgments -- Contents -- List of Figures -- List of Tables -- List of Abbreviations -- List of Equations -- Part I Basic Consideration -- Chapter 1 Introduction -- 1.1 Motivation -- 1.2 Objective -- 1.3 Method -- 1.4 Thesis Outline -- Chapter 2 Tumor Board Decision for Larynx Cancer Patients -- 2.1 Laryngeal Cancer -- 2.1.1 Epidemiology -- 2.1.2 Anatomy and Physiology -- 2.1.3 Cancer and Diagnostic -- 2.1.4 Therapy Decision of Laryngeal Cancer -- 2.2 Best Practice of Clinical Judgment -- 2.3 Head and Neck Tumor Board -- 2.3.1 Aims of a Tumor Board |
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2.3.2 Head and Neck TPU at Leipzig University Hospital -- Chapter 3 Development of a Clinical Decision Support System -- Introduction -- 3.1 Machine Intelligence in CDSSs -- 3.1.1 Non-knowledge-based System -- 3.1.2 Knowledge-based System -- 3.2 Impact of BN-based CDSS -- 3.3 Best Practice in Developing a CDSS -- 3.4 Integrated (model-based) Patient Care with MIMMS -- Chapter 4 Clinical Decision Model using Bayesian Networks -- 4.1 Bayesian Network -- 4.1.1 Bayesian Network Model -- 4.1.2 Inferencing -- 4.1.3 Methods for Model Analysis -- 4.1.4 Complexity -- 4.2 Model Design |
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4.2.1 Selection of Variables, States and their Identifiers -- 4.2.2 Building Dependencies and Causalities -- 4.2.3 Reducing the Effort in Modeling Parameters with Noisy-ORgates -- 4.3 Model Development -- 4.3.1 Machine Learning -- 4.3.2 Expert Modeling -- 4.4 Bayesian Network Extensions -- 4.4.1 Multi-Entity Bayesian Network -- 4.4.2 Influence Diagrams -- 4.5 Validation methods -- 4.5.1 Quantitative Validation -- 4.5.2 Qualitative Validation -- 4.6 Visualization of Probabilistic Graphical Models -- 4.7 Tools and Methods for Modeling, Validation and Visualization -- 4.7.1 GeNIe and SMILE |
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4.7.2 Domain-specific Modeling Tools -- Part II Therapy Decision Support System using Bayesian Networks -- Chapter 5 Patient-specific Bayesian Network in a Clinical Environment -- 5.1 Introduction -- 5.2 Decisions in an Oncological Therapy Workflow -- 5.3 Concept of a BN-based TDSS -- 5.4 Concept for Semi-automatic Quality Management of BN based TDSS -- Chapter 6 TreLynCa: A Tumor Board Decision Model for Laryngeal Cancer -- 6.1 Introduction -- 6.2 Teamwork and Knowledge Acquisition -- 6.3 Software and Tools for Modeling and Validation -- 6.4 Model Development -- 6.4.1 Model Structure |
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6.4.2 Modeling Variables and States -- 6.4.3 Modeling Probabilistic Parameters -- 6.5 Scope of the TreLynCa Model -- 6.6 Discussion -- 6.6.1 TreLynCa's Development -- 6.6.2 TreLynCa's Applicability -- Chapter 7 Model Validation -- 7.1 Introduction -- 7.2 Prediction in a Clinical Treatment Decision Context -- 7.3 Validation and Modification Workflow -- 7.4 TNM Model and Study Set-up -- 7.5 Quantitative Validation -- 7.5.1 Accuracy -- 7.5.2 ROC -- 7.5.3 Confusion Matrix -- 7.5.4 Calibration Curve -- 7.6 Qualitative Validation -- 7.6.1 Patient Record-based Model Validation |
| Summary |
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 |
| Bibliography |
Includes bibliographical references |
| Notes |
Description based on resource, viewed February 9, 2021 |
| Subject |
Medicine -- Decision making
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Decision support systems.
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Bayesian statistical decision theory.
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Larynx -- Cancer -- Treatment -- Decision making
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Decision making.
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Decision making -- Mathematical models.
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Decision Making
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Decision Support Techniques
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Decision Support Systems, Management
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decision making.
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Decision making -- Mathematical models
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Decision making
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Bayesian statistical decision theory
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Decision support systems
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Medicine -- Decision making
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| Form |
Electronic book
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| ISBN |
9783658325947 |
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3658325941 |
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