Distributionally Robust Optimization methods on robust medical diagnosis systems

Research Article
Open access

Distributionally Robust Optimization methods on robust medical diagnosis systems

Yumeng Tang 1* , Ziming Cui 2 , Xishi Wang 3 , Shuyu Xiang 4 , Yifeng Li 5
  • 1 Faculty of Electronic Information and Engineering, Tongji University, Shanghai, 201804, China    
  • 2 Case School of Engineering，Case Western Reserve University, Cleveland, 10900 Euclid Ave, Cleveland, OH 44106, America    
  • 3 School of Big Data and Information Engineering, Guizhou University, Guizhou, 550025, China,    
  • 4 Woodsworth college, University of Toronto, ON M5S1A1, Canada    
  • 5 Alevel, Changjun High School International Department, Changsha, 410013, China    
  • *corresponding author tonmoregulus@gmail.com
ACE Vol.44
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-327-2
ISBN (Online): 978-1-83558-328-9

Abstract

In the medical field, modern recommendation systems face significant challenges due to distributional shifts in data. We propose utilizing Distributionally Robust Optimization (DRO) and Distributionally and Outlier Robust Optimization (DORO) methods to address this issue. This paper aims to develop suitable DRO and DORO frameworks for the medical domain and validate their effectiveness through extensive experiments. We employ the DDXPlus dataset for our investigations and cluster patients based on age, sex, and initial evidence to partition the data into distinct distributions. Using a simple three-layer neural network, we incorporate CVaR and CHISQ as DRO methods and their respective DORO forms. The experimental results show that the overall DRO approach demonstrates more significant enhancements while all four methods exhibit improvements over the original distributional scenarios. Our research contributes to optimizing deep learning models in the medical domain and enhancing their robustness. Furthermore, we intend to use these methods to estimate and provide best-fit patient therapies, addressing real-world medical challenges. The application of these approaches has the potential to enhance the performance and practicality of medical recommendation systems, offering improved medical services to patients.

Keywords:

Distributionally Robust Optimization, Medical Diagnosis, DDXPlus dataset

Tang,Y.;Cui,Z.;Wang,X.;Xiang,S.;Li,Y. (2024). Distributionally Robust Optimization methods on robust medical diagnosis systems. Applied and Computational Engineering,44,99-107.
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References

[1]. Erick Delage, Yinyu Ye, Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems. Operations Research 58(3):595-612, 2010.

[2]. Fengming Lin, Xiaolei Fang, Zheming Gao. Distributionally Robust Optimization: A review on theory and applications. Numerical Algebra, Control and Optimization, 2022, 12(1): 159-212. doi: 10.3934/naco.2021057.

[3]. Hamed Rahimian, Sanjay Mehrotra, Distributionally Robust Optimization: A Review, in: Open Journal of Mathematical Optimization, Volume 3 (2022), article no. 4.

[4]. R. Zhai, C. Dan, J.Z. Kolter, P. Ravikumar, DORO: Distributional and Outlier Robust Optimization, in: Proceedings of the 38th International Conference on Machine Learning, PMLR 139:12345-12355, 2021.

[5]. A.F. Tchango, R. Goel, Z. Wen, J. Martel, J. Ghosn, DDXPlus: A New Dataset For Automatic Medical Diagnosis, in: Proceedings of the Neural Information Processing Systems-Track on Datasets and Benchmarks, 2, 2022.

[6]. Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, Xing Xie “On the Robustness of ChatGPT: An Adversarial and Out-ofdistribution Perspective” arXiv:2302.12095v4 [cs.AI] for this version.

[7]. Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford, Large-Scale Methods for Distributionally Robust Optimization, in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

[8]. Lancaster, H. O., and Seneta, E. (2005). Chi-square distribution, p. armitage and t. Colton in Encyclopedia of Biostatistics, Wiley, Chichester.

[9]. J.A. Hartigan, M.A. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, in: Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 28, No.1 (1979), pp. 100-108. DOI: https://doi.org/10.2307/2346830.

[10]. Ruidi Chen, Ioannis Ch. Paschalidis, A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization, in: Journal of Machine Learning Research 19 (2018) 1-48.

Cite this article

Tang,Y.;Cui,Z.;Wang,X.;Xiang,S.;Li,Y. (2024). Distributionally Robust Optimization methods on robust medical diagnosis systems. Applied and Computational Engineering,44,99-107.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-327-2(Print) / 978-1-83558-328-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.44
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Erick Delage, Yinyu Ye, Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems. Operations Research 58(3):595-612, 2010.

[2]. Fengming Lin, Xiaolei Fang, Zheming Gao. Distributionally Robust Optimization: A review on theory and applications. Numerical Algebra, Control and Optimization, 2022, 12(1): 159-212. doi: 10.3934/naco.2021057.

[3]. Hamed Rahimian, Sanjay Mehrotra, Distributionally Robust Optimization: A Review, in: Open Journal of Mathematical Optimization, Volume 3 (2022), article no. 4.

[4]. R. Zhai, C. Dan, J.Z. Kolter, P. Ravikumar, DORO: Distributional and Outlier Robust Optimization, in: Proceedings of the 38th International Conference on Machine Learning, PMLR 139:12345-12355, 2021.

[5]. A.F. Tchango, R. Goel, Z. Wen, J. Martel, J. Ghosn, DDXPlus: A New Dataset For Automatic Medical Diagnosis, in: Proceedings of the Neural Information Processing Systems-Track on Datasets and Benchmarks, 2, 2022.

[6]. Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, Xing Xie “On the Robustness of ChatGPT: An Adversarial and Out-ofdistribution Perspective” arXiv:2302.12095v4 [cs.AI] for this version.

[7]. Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford, Large-Scale Methods for Distributionally Robust Optimization, in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

[8]. Lancaster, H. O., and Seneta, E. (2005). Chi-square distribution, p. armitage and t. Colton in Encyclopedia of Biostatistics, Wiley, Chichester.

[9]. J.A. Hartigan, M.A. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, in: Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 28, No.1 (1979), pp. 100-108. DOI: https://doi.org/10.2307/2346830.

[10]. Ruidi Chen, Ioannis Ch. Paschalidis, A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization, in: Journal of Machine Learning Research 19 (2018) 1-48.

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