Therapy Decision Support System using Bayesian Networks for Multidisciplinary Treatment Decisions

Treatment decision-making in head and neck oncology is gaining complexity by the increasing evidence pointing towards more individualized and selective treatment options. Therefore, decision making in multidisciplinary teams is becoming the key point in the clinical pathways. Clinical decision-support systems based on Bayesian networks can support complex decision-making processes by providing mathematically correct and transparent advises. In the last three decades, different clinical applications of Bayesian networks have been proposed. Because appropriate data for model learning and testing is often unobtainable, expert modeling is required. To decrease the modeling and validation effort, networks usually represent small or highly simplified decision structures. However, especially systems for supporting multidisciplinary treatment decisions may only gain a user’s confidence if the systems’ results are comprehensive and comprehensible. Challenges in developing such systems relate to knowledge engineering, model validation, system interaction, clinical implementation and standardization. These challenges are well-known, however, they are not or only partially addressed by the developers. The thesis presented a methodology for the development of Bayesian network-based clinical treatment decision support systems. For this purpose, a concept introduced interactions between actors and systems. The proposed concept emphasizes model development with an exemplary use case of model interaction. A graph model design was presented that allows integrating all relevant variables of multidisciplinary treatment decisions. At the current stage, we developed TreLynCa: A graph model representing the treatment decisions of laryngeal cancer. From TreLynCa, a subnetwork that represents the TNM staging is completed by the required probabilistic parameters, and finally validated. The model validation required the development of a validation cycle in combination with existing data- and expert-based validation methods. Furthermore, modeling methods were developed that enable domain experts to model autonomously without Bayesian network expertise. Specifically, a novel graph modeling method was developed, and an existing method for modeling probabilistic parameters was extended. Both methods transform Bayesian network modeling tasks into a natural language form and provide a regulated modeling environment. A method for graph modeling is based on the presented graph model design with a regulated and restricted modeling procedure. This modeling procedure is supposed to enable collaborative modeling of compatible models. The method is currently under development. A method for probabilistic modeling is extended to reduce the modeling effort to a linear time. The method has been implemented as a web tool and was tested and evaluated in two studies. Finally, for clinical application of the TNM model, requirements were collected and constructed in a visual framework. In collaboration with visual scientists, the framework has been implemented and evaluated