Digital patient models based on Bayesian networks for clinical treatment decision support

Abstract

Increasing complexity in the management of oncologic diseases due to advances in diagnostics and individualized treatments demands new techniques of comprehensive decision support. Digital patient models (DPMs) are developed to collect, structure, and evaluate information to improve the decision-making process in tumour boards and surgical procedures in the operating room (OR). Laryngeal cancer (LC) was selected as a prototype to build a clinical decision support system (CDSS) based on Bayesian networks (BN). The model was built in cooperation with a knowledge engineer and a domain expert in head and neck oncology. Once a CDSS is developed, individual patient data can be set to compute a patient-specific BN. The modelling was based on clinical guidelines and analysis of the tumour board decision making. Besides description of the modelling process, recommendations for standardised modelling, new tools, validation and interaction of extensive models are presented. The LC model contains over 1,000 variables with about 1,300 dependencies. A subnetwork representing TNM staging (303 variables) was validated and reached 100% of correct model predictions. Given the new methods and tools, construction of a complex human-readable CDSS is feasible. Interactive platforms with guided modelling may support collaborative model development and extension to other diseases. Appropriate tools may assist decision making in various situations, e.g. the OR.