DeepAI AI Chat
Log In Sign Up

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

by   Hao Wang, et al.
Columbia University

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.


page 1

page 2

page 3

page 4


Workload-Aware Materialization of Junction Trees

Bayesian networks are popular probabilistic models that capture the cond...

Learning Structures of Bayesian Networks for Variable Groups

Bayesian networks, and especially their structures, are powerful tools f...

Designing neural networks that process mean values of random variables

We introduce a class of neural networks derived from probabilistic model...

Natural-Parameter Networks: A Class of Probabilistic Neural Networks

Neural networks (NN) have achieved state-of-the-art performance in vario...

Prequential MDL for Causal Structure Learning with Neural Networks

Learning the structure of Bayesian networks and causal relationships fro...

Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks

Algorithms for learning the conditional probabilities of Bayesian networ...

Network Learning Approaches to study World Happiness

The United Nations in its 2011 resolution declared the pursuit of happin...