Approximate Inference for Medical Diagnosis

Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in health care. Due to the inherent probabilistic nature of medical diagnosis, a DSS should preferably be based on a probabilistic model. In particular Bayesian networks provide a powerful and conceptually transparent formalism for probabilistic modeling. A drawback is that Bayesian networks become intractable for exact computation if a large medical domain would be modeled in detail. This has obstructed the development of a useful system for internal medicine. Advances in approximation techniques, e.g. using variational methods with tractable structures, have opened new possibilities to deal with the computational problem. However, the only way to assess the usefulness of these methods for a DSS in practice is by actually building such a system and evaluating it by users. In the coming years, we aim to build a DSS for anaemia based on a detailed probabilistic model, and equipped with appr..