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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

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

February 1, 2023

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Authors

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


DOI:

10.1609/aaai.v33i01.3301766


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.

Topics: AAAI

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HOW TO CITE:

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling Proceedings of the AAAI Conference on Artificial Intelligence (2019) 766-773.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling AAAI 2019, 766-773.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi (2019). Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling. Proceedings of the AAAI Conference on Artificial Intelligence, 766-773.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi. Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.766-773.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi. 2019. Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling. "Proceedings of the AAAI Conference on Artificial Intelligence". 766-773.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi. (2019) "Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling", Proceedings of the AAAI Conference on Artificial Intelligence, p.766-773

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi, "Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling", AAAI, p.766-773, 2019.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi. "Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.766-773.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi. "Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 766-773.

Hao Wang||Chengzhi Mao||Hao He||Mingmin Zhao||Tommi S. Jaakkola||Dina Katabi. Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling. AAAI[Internet]. 2019[cited 2023]; 766-773.


ISSN: 2374-3468


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