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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis

February 1, 2023

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Authors

Yuan Xia

Baidu Inc.


Jingbo Zhou

Baidu Inc.


Zhenhui Shi

Baidu Inc.


Chao Lu

Baidu Inc.


Haifeng Huang

Baidu Inc.


DOI:

10.1609/aaai.v34i01.5456


Abstract:

Automatic diagnosis systems have attracted increasing attention in recent years. The reinforcement learning (RL) is an attractive technique for building an automatic diagnosis system due to its advantages for handling sequential decision making problem. However, the RL method still cannot achieve good enough prediction accuracy. In this paper, we propose a Generative Adversarial regularized Mutual information Policy gradient framework (GAMP) for automatic diagnosis which aims to make a diagnosis rapidly and accurately. We first propose a new policy gradient framework based on the Generative Adversarial Network (GAN) to optimize the RL model for automatic diagnosis. In our framework, we take the generator of GAN as a policy network, and also use the discriminator of GAN as a part of the reward function. This generative adversarial regularized policy gradient framework can try to avoid generating randomized trials of symptom inquires deviated from the common diagnosis paradigm. In addition, we add mutual information to enhance the reward function to encourage the model to select the most discriminative symptoms to make a diagnosis. Experiment evaluations on two public datasets show that our method beats the state-of-art methods, not only can achieve higher diagnosis accuracy, but also can use a smaller number of inquires to make diagnosis decision.

Topics: AAAI

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HOW TO CITE:

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis Proceedings of the AAAI Conference on Artificial Intelligence (2020) 1062-1069.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis AAAI 2020, 1062-1069.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang (2020). Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis. Proceedings of the AAAI Conference on Artificial Intelligence, 1062-1069.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang. Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.1062-1069.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang. 2020. Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis. "Proceedings of the AAAI Conference on Artificial Intelligence". 1062-1069.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang. (2020) "Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis", Proceedings of the AAAI Conference on Artificial Intelligence, p.1062-1069

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang, "Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis", AAAI, p.1062-1069, 2020.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang. "Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.1062-1069.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang. "Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 1062-1069.

Yuan Xia||Jingbo Zhou||Zhenhui Shi||Chao Lu||Haifeng Huang. Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis. AAAI[Internet]. 2020[cited 2023]; 1062-1069.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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