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Indigo Development of Clinical Decision Support Systems using Bayesian Networks: With an example of a Multi-Disciplinary Treatment Decision for Laryngeal Cancer | Erin Mills Town Centre
Development of Clinical Decision Support Systems using Bayesian Networks: With an example of a Multi-Disciplinary Treatment Decision for Laryngeal Cancer
Development of Clinical Decision Support Systems using Bayesian Networks: With an example of a Multi-Disciplinary Treatment Decision for Laryngeal Cancer
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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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.
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.
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