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.
Author(s): Mario A. Cypko
Publisher: Springer Vieweg
Year: 2020
Language: English
Pages: 148
City: Wiesbaden
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
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
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
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
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
7.6.2 Subnetwork Validation
7.7 Results and Modifications
7.8 Discussion and Conclusion
Chapter 8 Tools for Guided BN Modeling
8.1 Introduction
8.2 Concept for Guided Modeling and Validation
8.2.1 Requirements for an Autonomous Expert Modeling and Validation
8.2.2 Concept of a Web-based Expert Modeling System
8.3 Graph Modeling Tool
8.3.1 Conceptual Framework of a Graph Modeling Tool
8.3.2 Guided Graph Modeling
8.3.3 Study Set-up
8.3.4 Study Results
8.3.5 Discussion
8.3.6 Conclusion
8.4 CPT Modeling Tool
8.4.1 Questionnaire to Find Dominant Parent States
8.4.2 Web-based Framework for CPT Assessment
8.4.3 Study Designs
8.4.4 Results
8.4.5 Discussion
8.5 Conclusion
Chapter 9 GUI for PSBN-based decision verification
9.1 Introduction
9.2 Motivation for TNM Verification
9.3 Requirements for a TNM Verification GUI
9.4 Verification Framework
9.5 Graph View Design
9.6 User Workflow
9.7 Study Set-up of TNM Verification
9.8 Results
9.9 Discussion
9.9.1 Study results and limitations
9.9.2 Layout
9.9.3 Functionality
9.9.4 Applicability
9.10 Conclusion
Part III Conclusion
Chapter 10 Summary
Chapter 11 Appraisal and Future Work
11.1 Applicability of Methodologies and Tools
11.2 Compliance of Requirements
11.3 Impact of Decision Graphs
11.4 Storage and Cross-Sharing of BN and PSBN
11.5 Reasoned PSBN Justification
11.6 Appropriate Patient Data Quality
11.7 Extension to Multi-Entity Decision Graphs
Bibliography
List of Relevant Publications
Appendix A TNM staging by the NCCN guidelines
