M.J. Nijman

Approximate inference for medical diagnosis

Abstract Computer-based diagnostic decision support systems (DSSs) 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 is to 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 approximate methods to study the practical feasibility and the usefulness of this approach in medical practice. In this paper, we will sketch how variational techniques with tractable structures can be used in a typical model for medical diagnosis. We provide numerical results on artificial problems. In addition, we describe our approach to develop the Bayesian network for the DSS and show some preliminary results.

Efficient Learning in Sparsely Connected Boltzmann Machines

We present a heuristical procedure for efficient estimation of the partition function in the Boltzmann distribution. The resulting speed-up is of immediate relevance for the speed-up of Boltzmann Machine learning rules, especially for networks with a sparse connectivity.