Transferability modelling in the TREAT decision support system

One of the key-components for success of a decision support system is in its flexibility and applicability to different clinical locations. The present study is devoted to a system which is capable of successful transfer to a distant environment. We have developed a decision support system for antibiotic treatment (TREAT), which was adapted to four different hospitals in Europe. The system is based on a causal probabilistic network (CPN). The purpose of this paper is to present the models for transferability used in TREAT. The problem of transferability is addressed in the context of CPNs, emphasising the advantages of use of CPNs for solving the problem. The process of adapting TREAT is relatively easy; that is due to the modularity of the system. The system has been built using a modular architecture that allows rapid transfer of the system to different clinical environments. Such modularity can be archived by simple means which include the universal and modular structure of the CPN, the establishment of a large group of conditional probabilities in the CPN that are assumed to be independent of time and place, and the use of hierarchical Dirichlet methods for learning of data. Due to the universal structure of the CPN, the problem of transferability in TREAT concerns only the medical domain factors, not the topology of the system.