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Learning by Reasoning: An Explainable Hierarchical Association Regularized Deep Learning Method for Disease Prediction

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HCI in Business, Government and Organizations (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14038))

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Abstract

Multimodal healthcare data provides a huge opportunity for big-data-based disease prediction, supporting the diagnosis and treatment decision-making process for doctors. Many deep learning based methods are developed to yield better performance of multimodal data based disease prediction considering their powerful representation abilities. However, the black-box nature of deep learning methods results in many serious concerns: e.g. the reliability of the prediction performance is questionable; the end-users (i.e., doctors) can not understand the reasons behind the prediction. These issues make it difficult for deep learning based disease prediction systems to apply in practice. Therefore, we aim to tackle the aforementioned challenges and propose an explainable hierarchical association regularized deep learning method to produce interpretable prediction results while maintaining prediction accuracy. The method takes the multimodal healthcare data into consideration for the disease prediction task and constructs a hierarchical association path for each sample. An ingenious loss function is designed to learn consistent features among different data modalities and the disease prediction path with a hierarchical structure. The experimental results based on a public dataset show the superiority of our proposed method. The ablation study and sensitivity analysis verify the effectiveness and necessity of the method design.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant # 71971067, 72271059), the National Social Science Foundation of China (grant # 22AZD136), the Research Fund Program of Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization (grant # 2021501), the Shanghai "Science and Technology Innovation Action Plan" Soft Science Research Project (grant # 22692108300), and the China Postdoctoral Science Foundation, (grant # 2022M722394).

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Authors and Affiliations

  1. School of Economics and Management, Tongji University, Shanghai, 200092, People’s Republic of China

    Shuaiyong Xiao & Jie Lin

  2. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, 510632, People’s Republic of China

    Shuaiyong Xiao

  3. School of Management, Zhejiang University, Hangzhou, 310058, People’s Republic of China

    Gang Chen

  4. School of Management, Fudan University, Shanghai, 200433, People’s Republic of China

    Zongxiang Zhang & Chenghong Zhang

Authors
  1. Shuaiyong Xiao
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  2. Gang Chen
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  4. Chenghong Zhang
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  5. Jie Lin
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Corresponding author

Correspondence to Chenghong Zhang .

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Editors and Affiliations

  1. City University of Hong Kong, Kowloon, Hong Kong

    Fiona Nah

  2. City University of Hong Kong, Kowloon, Hong Kong

    Keng Siau

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Xiao, S., Chen, G., Zhang, Z., Zhang, C., Lin, J. (2023). Learning by Reasoning: An Explainable Hierarchical Association Regularized Deep Learning Method for Disease Prediction. In: Nah, F., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2023. Lecture Notes in Computer Science, vol 14038. Springer, Cham. https://doi.org/10.1007/978-3-031-35969-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-35969-9_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-35969-9

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