Variational hybridization and transformation for large inaccurate noisy-or networks
This work addresses the problem of scalable and stable medical diagnosis for large-scale applications, though it appears incremental as it builds on existing variational transformation methods.
The paper tackled the challenge of performing reliable and real-time diagnosis at web scale using noisy-or Bayesian networks by proposing a hybridized inference method that eliminates the need for disease posteriors or priors, resulting in faster computation and recyclable components, along with a transformation ranking algorithm stable to large variances in prior probabilities. In experiments, they demonstrated scalability on a synthesized network 36,000 times larger than a real-life medical network.
Variational inference provides approximations to the computationally intractable posterior distribution in Bayesian networks. A prominent medical application of noisy-or Bayesian network is to infer potential diseases given observed symptoms. Previous studies focus on approximating a handful of complicated pathological cases using variational transformation. Our goal is to use variational transformation as part of a novel hybridized inference for serving reliable and real time diagnosis at web scale. We propose a hybridized inference that allows variational parameters to be estimated without disease posteriors or priors, making the inference faster and much of its computation recyclable. In addition, we propose a transformation ranking algorithm that is very stable to large variances in network prior probabilities, a common issue that arises in medical applications of Bayesian networks. In experiments, we perform comparative study on a large real life medical network and scalability study on a much larger (36,000x) synthesized network.