Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling

  • Hao Wang MIT CSAIL
  • Chengzhi Mao Columbia University
  • Hao He MIT CSAIL
  • Mingmin Zhao MIT CSAIL
  • Tommi S. Jaakkola MIT CSAIL
  • Dina Katabi MIT CSAIL

Abstract

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.

Published
2019-07-17
Section
AAAI Special Technical Track: AI for Social Impact