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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Beschreibung
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.
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
About the AuthorDr.-Ing. Mario A. Cypko completed his PhD at the Computer Science department of the University of Leipzig, Germany. He was a postdoctoral research fellow in the Human Research Office of the European Space Agency in the Netherlands. He is currently a postdoctoral research assistant at the German Heart Center Berlin, Germany.
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.
New opportunities for completely transparent and reproducible CDSS
Autor*in
Mario A. Cypko
Themen in »Development of Clinical Decision Support Systems using Bayesian Networks«
Clinical Decision Support System Bayesian Network Cancer treatment Decision models Guided modelling and validation Tumorboard Laryngeal Cancer Semi-automatic modelling Model-user interaction Inter-institutional modelling Network in a Clinical Environment TreLynCa Guided BN Modeling PSBN-based decision verification