skip to main content
10.1145/3511808.3557440acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

RSD: A Reinforced Siamese Network with Domain Knowledge for Early Diagnosis

Published: 17 October 2022 Publication History

Abstract

The availability of electronic health record data makes it possible to develop automatic disease diagnosis approaches. In this paper, we study the early diagnosis of diseases. As being a difficult task (even for experienced doctors), early diagnosis of diseases poses several challenges that are not well solved by prior studies, including insufficient training data, dynamic and complex signs of complications and trade-off between earliness and accuracy.
To address these challenges, we propose a <u>R</u>einforced <u>S</u>iamese network with <u>D</u>omain knowledge regularization approach, namely RSD, to achieve high performance for early diagnosis. The RSD approach consists of a diagnosis module and a control module. The diagnosis module adopts any EHR Encoder as a basic framework to extract representations, and introduces two improved training strategies. To overcome the insufficient sample problem, we design a Siamese network architecture to enhance the model learning. Furthermore, we propose a domain knowledge regularization strategy to guide the model learning with domain knowledge. Based on the diagnosis module, our control module learns to automatically determine whether making a disease alert to the patients based on the diagnosis results. Through carefully designed architecture, rewards and policies, it is able to effectively balance earliness and accuracy for diagnosis. Experimental results have demonstrated the effectiveness of our approach on both diagnosis prediction and early diagnosis. We also perform extensive analysis experiments to verify the robustness of the proposed approach.

References

[1]
Tian Bai, Shanshan Zhang, Brian L. Egleston, and Slobodan Vucetic. 2018. Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time. In KDD. ACM, 43--51.
[2]
Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, and Jiayu Zhou. 2017. Patient Subtyping via Time-Aware LS Networks. In KDD. ACM, 65--74.
[3]
Ling Chen, Xue Li, Quan Z. Sheng, Wen-Chih Peng, John Bennett, Hsiao-Yun Hu, and Nicole Huang. 2016. Mining Health Examination Records - A Graph-Based Approach. IEEE Trans. Knowl. Data Eng., Vol. 28, 9 (2016), 2423--2437.
[4]
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597--1607. http://proceedings.mlr.press/v119/chen20j.html
[5]
Yu Cheng, Fei Wang, Ping Zhang, and Jianying Hu. 2016. Risk Prediction with Electronic Health Records: A Deep Learning Approach. In SDM. SIAM, 432--440.
[6]
Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, and Jimeng Sun. 2017. GRAM: Graph-based Attention Model for Healthcare Representation Learning. In KDD. ACM, 787--795.
[7]
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter F. Stewart. 2016. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In NIPS. 3504--3512.
[8]
Sumit Chopra, Raia Hadsell, and Yann LeCun. 2005. Learning a Similarity Metric Discriminatively, with Application to Face Verification. In CVPR (1). IEEE Computer Society, 539--546.
[9]
Kuzman Ganchev, Jo a o Gracc a, Jennifer Gillenwater, and Ben Taskar. 2010. Posterior Regularization for Structured Latent Variable Models. J. Mach. Learn. Res., Vol. 11 (2010), 2001--2049.
[10]
Mohamed F. Ghalwash, Vladan Radosavljevic, and Zoran Obradovic. 2014. Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In KDD. ACM, 402--411.
[11]
Edward W Gregg, Qiuping Gu, Yiling J Cheng, KM Venkat Narayan, and Catherine C Cowie. 2007. Mortality trends in men and women with diabetes, 1971 to 2000. Annals of internal medicine, Vol. 147, 3 (2007), 149--155.
[12]
Shiying Hao, Karl G. Sylvester, Xuefeng Bruce Ling, Andrew Young Shin, Zhongkai Hu, Bo Jin, Chunqing Zhu, Dorothy Dai, Frank Stearns, Eric Widen, Devore S. Culver, Shaun T. Alfreds, and Todd Rogow. 2015. Risk prediction for future 6-month healthcare resource utilization in Maine. In BIBM. IEEE Computer Society, 863--866.
[13]
Wendy W Harrison and Vladimir Yevseyenkov. 2015. Early interventions to prevent retinal vasculopathy in diabetes: a review. Clinical Optometry, Vol. 7 (2015), 71.
[14]
Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, and Elke A. Rundensteiner. 2019. Adaptive-Halting Policy Network for Early Classification. In KDD. ACM, 101--110.
[15]
Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg, and Aram Galstyan. 2019. Multitask learning and benchmarking with clinical time series data. Scientific Data, Vol. 6, 1 (2019), 96. https://doi.org/10.1038/s41597-019-0103-9
[16]
Nima Hatami and Camelia Chira. 2013. Classifiers with a reject option for early time-series classification. In Proceedings of the IEEE Symposium on Computational Intelligence and Ensemble Learning, CIEL 2013, IEEE Symposium Series on Computational Intelligence (SSCI), 16--19 April 2013, Singapore. IEEE, 9--16. https://doi.org/10.1109/CIEL.2013.6613134
[17]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR. IEEE Computer Society, 770--778.
[18]
Barbara Heude, Olivier Thiébaugeorges, Valérie Goua, Anne Forhan, Monique Kaminski, Bernard Foliguet, Michel Schweitzer, Guillaume Magnin, Marie-Aline Charles, EDEN Mother-Child Cohort Study Group, et al. 2012. Pre-pregnancy body mass index and weight gain during pregnancy: relations with gestational diabetes and Hypertension, and birth outcomes. Maternal and child health journal, Vol. 16, 2 (2012), 355--363.
[19]
Sepp Hochreiter and Jü rgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation, Vol. 9, 8 (1997), 1735--1780.
[20]
Henry Hsu and Peter A Lachenbruch. 2014. Paired t test. Wiley StatsRef: statistics reference online (2014).
[21]
Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015 (JMLR Workshop and Conference Proceedings, Vol. 37), Francis R. Bach and David M. Blei (Eds.). JMLR.org, 448--456. http://proceedings.mlr.press/v37/ioffe15.html
[22]
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-Wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific data, Vol. 3, 1 (2016), 1--9.
[23]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR (Poster).
[24]
Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, and Jaegul Choo. 2019. RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records. IEEE Trans. Vis. Comput. Graph., Vol. 25, 1 (2019), 299--309.
[25]
Zhaoqian Lan, Guopeng Zhou, Yichun Duan, and Wei Yan. 2018. AI-Assisted Prediction on Potential Health Risks with Regular Physical Examination Records. In DSC. IEEE, 346--352.
[26]
Yikuan Li, Shishir Rao, Jose Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi. 2020. BEHRT: transformer for electronic health records. Scientific reports, Vol. 10, 1 (2020), 1--12.
[27]
Andy Liaw, Matthew Wiener, et al. 2002. Classification and regression by randomForest. R news, Vol. 2, 3 (2002), 18--22.
[28]
Junyu Luo, Muchao Ye, Cao Xiao, and Fenglong Ma. 2020. HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records. In KDD. ACM, 647--656.
[29]
Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao. 2017. Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks. In KDD. ACM, 1903--1911.
[30]
Fenglong Ma, Jing Gao, Qiuling Suo, Quanzeng You, Jing Zhou, and Aidong Zhang. 2018a. Risk Prediction on Electronic Health Records with Prior Medical Knowledge. In KDD. ACM, 1910--1919.
[31]
Fenglong Ma, Quanzeng You, Houping Xiao, Radha Chitta, Jing Zhou, and Jing Gao. 2018b. KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare. In CIKM. ACM, 743--752.
[32]
Coralie Martinez, Emmanuel Ramasso, Guillaume Perrin, and Michè le Rombaut. 2020. Adaptive early classification of temporal sequences using deep reinforcement learning. Knowl. Based Syst., Vol. 190 (2020), 105290.
[33]
Usue Mori, Alexander Mendiburu, Sanjoy Dasgupta, and José Antonio Lozano. 2018. Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness. IEEE Trans. Neural Networks Learn. Syst., Vol. 29, 10 (2018), 4569--4578.
[34]
Usue Mori, Alexander Mendiburu, Eamonn J. Keogh, and José Antonio Lozano. 2017. Reliable early classification of time series based on discriminating the classes over time. Data Min. Knowl. Discov., Vol. 31, 1 (2017), 233--263.
[35]
World Health Organization et al. 2019. World health statistics overview 2019: monitoring health for the SDGs, sustainable development goals. Technical Report. World Health Organization.
[36]
P. K. S. Prakash, Srinivas Chilukuri, Nikhil Ranade, and Shankar Viswanathan. 2021. RareBERT: Transformer Architecture for Rare Disease Patient Identification using Administrative Claims. In AAA21. AAAI Press, 453--460.
[37]
Houxing Ren, Jingyuan Wang, and Wayne Xin Zhao. 2022. Generative Adversarial Networks Enhanced Pre-Training for Insufficient Electronic Health Records Modeling. In KDD. ACM, 3810--3818.
[38]
Houxing Ren, Jingyuan Wang, Wayne Xin Zhao, and Ning Wu. 2021. RAPT: Pre-training of Time-Aware Transformer for Learning Robust Healthcare Representation. In KDD. ACM, 3503--3511.
[39]
Marc Rußwurm, Sé bastien Lefè vre, Nicolas Courty, Ré mi Emonet, Marco Kö rner, and Romain Tavenard. 2019. End-to-end Learning for Early Classification of Time Series. CoRR, Vol. abs/1901.10681 (2019).
[40]
Patrick Schäfer and Ulf Leser. 2020. TEASER: early and accurate time series classification. Data Min. Knowl. Discov., Vol. 34, 5 (2020), 1336--1362.
[41]
Akihiro Shimoda, Daisuke Ichikawa, and Hiroshi Oyama. 2018. Using machine-learning approaches to predict non-participation in a nationwide general health check-up schemeComput. Methods Programs Biomed., Vol. 163 (2018), 39--46.
[42]
Baha M Sibai. 2003. Diagnosis and management of gestational Hypertension and preeclampsia. Obstetrics & Gynecology, Vol. 102, 1 (2003181-192.
[43]
Priya Soma-Pillay, Nelson-Piercy Catherine, Heli Tolppanen, Alexandre Mebazaa, Heli Tolppanen, and Alexandre Mebazaa. 2016. Physiological changes in pregnancy. Cardiovascular journal of Africa, Vol. 27, 2 (2016), 89.
[44]
Karen Tu, Zhongliang Chen, Lorraine L Lipscombe, et al. 2008. Mortality among patients with hypertension from 1995 to 2005: a population-based study. Cmaj, Vol. 178, 11 (2008), 1436--1440.
[45]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008.
[46]
Ronald J. Williams. 1992. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning. Mach. Learn., Vol. 8 (1992), 229--256.
[47]
Zhengzheng Xing, Jian Pei, Guozhu Dong, and Philip S. Yu. 2008. Mining Sequence Classifiers for Early Prediction. In SDM. SIAM, 644--655.
[48]
Zhengzheng Xing, Jian Pei, and Philip S. Yu. 2009. Early Prediction on Time Series: A Nearest Neighbor Approach. In IJCAI. 1297--1302.
[49]
Liuyi Yao, Yaliang Li, Yezheng Li, Hengtong Zhang, Mengdi Huai, Jing Gao, and Aidong Zhang. 2019. DTEC: Distance Transformation Based Early Time Series Classification. In SDM. SIAM, 486--494.
[50]
Xi Sheryl Zhang, Fengyi Tang, Hiroko H. Dodge, Jiayu Zhou, and Fei Wang. 2019. MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records. In KDD. ACM, 2487--2495.

Cited By

View all
  • (2024)ProtoMix: Augmenting Health Status Representation Learning via Prototype-based MixupProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671937(3633-3644)Online publication date: 25-Aug-2024
  • (2023)CALIMERAInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346560:5Online publication date: 1-Sep-2023

Index Terms

  1. RSD: A Reinforced Siamese Network with Domain Knowledge for Early Diagnosis

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
    October 2022
    5274 pages
    ISBN:9781450392365
    DOI:10.1145/3511808
    • General Chairs:
    • Mohammad Al Hasan,
    • Li Xiong
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 October 2022

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. early diagnosis
    2. reinforcement learning
    3. siamese network

    Qualifiers

    • Research-article

    Funding Sources

    • the Fundamental Research Funds for the Central Universities
    • the National Natural Science Foundation of China
    • the National Key R&D Program of China

    Conference

    CIKM '22
    Sponsor:

    Acceptance Rates

    CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

    Upcoming Conference

    CIKM '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)66
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 12 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)ProtoMix: Augmenting Health Status Representation Learning via Prototype-based MixupProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671937(3633-3644)Online publication date: 25-Aug-2024
    • (2023)CALIMERAInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346560:5Online publication date: 1-Sep-2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media