short-paper

Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals

Authors:

Leye WangAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 3987 - 3993

https://doi.org/10.1145/3543507.3583989

Published: 30 April 2023 Publication History

Abstract

There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals and promote health equity through knowledge sharing among medical institutions. Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease. The Sepsis-3 guideline, published in 2016, defines that sepsis can be diagnosed by satisfying both suspicion of infection and Sequential Organ Failure Assessment (SOFA) greater than or equal to 2. Based on this knowledge, SofaNet adopts a multi-channel GRU structure to predict SOFA values of different systems, which can be seen as an auxiliary task to generate better health status representations for sepsis recognition. Moreover, we only achieve feature distribution alignment in the hidden space during cross-center collaborative learning, which ensures secure and compliant knowledge transfer without raw data exchange. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SofaNet can benefit early sepsis recognition when hospitals only have limited EMRs.

References

[1]

Edward Choi, Cao Xiao, Walter Stewart, and Jimeng Sun. 2018. Mime: Multilevel medical embedding of electronic health records for predictive healthcare. Advances in neural information processing systems 31 (2018).

[2]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

[3]

KJKIS Disease. 2012. Improving global outcomes (KDIGO) acute kidney injury work group: KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl 2, 1 (2012), 1–138.

[4]

John Anda Du, Nadi Sadr, and Philip de Chazal. 2019. Automated prediction of sepsis onset using gradient boosted decision trees. In 2019 Computing in Cardiology (CinC). IEEE, Page–1.

[5]

Ricard Ferrer, Antonio Artigas, Mitchell M Levy, Jesus Blanco, Gumersindo Gonzalez-Diaz, José Garnacho-Montero, Jordi Ibáñez, Eduardo Palencia, Manuel Quintana, María Victoria De La Torre-Prados, 2008. Improvement in process of care and outcome after a multicenter severe sepsis educational program in Spain. Jama 299, 19 (2008), 2294–2303.

[6]

Lucas M Fleuren, Thomas LT Klausch, Charlotte L Zwager, Linda J Schoonmade, Tingjie Guo, Luca F Roggeveen, Eleonora L Swart, Armand RJ Girbes, Patrick Thoral, Ari Ercole, 2020. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive care medicine 46, 3 (2020), 383–400.

[7]

Joseph Futoma, Sanjay Hariharan, and Katherine Heller. 2017. Learning to detect sepsis with a multitask Gaussian process RNN classifier. In International Conference on Machine Learning. PMLR, 1174–1182.

[8]

Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised Domain Adaptation by Backpropagation. In Proceedings of the 32nd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 37), Francis Bach and David Blei (Eds.). PMLR, Lille, France, 1180–1189.

[9]

Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation 101, 23 (2000), e215–e220.

[10]

Priyanka Gupta, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, and Gautam Shroff. 2020. Transfer learning for clinical time series analysis using deep neural networks. Journal of Healthcare Informatics Research 4, 2 (2020), 112–137.

[11]

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 3, 1 (2016), 1–9.

[12]

Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, 2020. Privacy-preserving technology to help millions of people: Federated prediction model for stroke prevention. arXiv preprint arXiv:2006.10517 (2020).

[13]

Mohammad Kachuee, Shayan Fazeli, and Majid Sarrafzadeh. 2018. Ecg heartbeat classification: A deep transferable representation. In 2018 IEEE international conference on healthcare informatics (ICHI). IEEE, 443–444.

[14]

Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, and Vipin Kumar. 2018. Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering 31, 8 (2018), 1544–1554.

[15]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[16]

Anand Kumar, Daniel Roberts, Kenneth E Wood, Bruce Light, Joseph E Parrillo, Satendra Sharma, Robert Suppes, Daniel Feinstein, Sergio Zanotti, Leo Taiberg, 2006. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Critical care medicine 34, 6 (2006), 1589–1596.

[17]

Gyemin Lee, Ilan Rubinfeld, and Zeeshan Syed. 2012. Adapting surgical models to individual hospitals using transfer learning. In 2012 IEEE 12th international conference on data mining workshops. IEEE, 57–63.

[18]

Jared Leitner, Po-Han Chiang, and Sujit Dey. 2021. Personalized blood pressure estimation using photoplethysmography: A transfer learning approach. IEEE Journal of Biomedical and Health Informatics 26, 1 (2021), 218–228.

[19]

Mitchell M. Levy, Mitchell P. Fink, John C. Marshall, Edward Abraham, Derek Angus, Deborah Cook, Jonathan Cohen, Steven M. Opal, Jean-Louis Vincent, Graham Ramsay, and for the International Sepsis Definitions Conference. 2003. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. 29, 4 (2003), 530–538.

[20]

Xiang Li, Xiao Xu, Fei Xie, Xian Xu, Yuyao Sun, Xiaoshuang Liu, Xiaoyu Jia, Yanni Kang, Lixin Xie, Fei Wang, 2020. A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care. Critical Care Medicine 48, 10 (2020), e884–e888.

[21]

Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S Yu. 2020. Privacy and robustness in federated learning: Attacks and defenses. arXiv preprint arXiv:2012.06337 (2020).

[22]

Liantao Ma, Xinyu Ma, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Chaohe Zhang, Wenjie Ruan, Yasha Wang, Wen Tang, and Jiangtao Wang. 2021. Distilling knowledge from publicly available online EMR data to emerging epidemic for prognosis. In Proceedings of the Web Conference 2021. 3558–3568.

[23]

Liantao Ma, Chaohe Zhang, Yasha Wang, Wenjie Ruan, Jiangtao Wang, Wen Tang, Xinyu Ma, Xin Gao, and Junyi Gao. 2020. Concare: Personalized clinical feature embedding via capturing the healthcare context. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 833–840.

[24]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273–1282.

[25]

Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley. 2018. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics 19, 6 (2018), 1236–1246.

[26]

James Morrill, Andrey Kormilitzin, Alejo Nevado-Holgado, Sumanth Swaminathan, Sam Howison, and Terry Lyons. 2019. The signature-based model for early detection of sepsis from electronic health records in the intensive care unit. In 2019 Computing in Cardiology (CinC). IEEE, Page–1.

[27]

American College of Chest Physicians, Society of Critical Care Medicine Consensus Conference Committee, 1992. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit. Care Med 20 (1992), 864–874.

[28]

Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang. 2010. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks 22, 2 (2010), 199–210.

[29]

Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2009), 1345–1359.

[30]

Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko. 2019. Federated Adversarial Domain Adaptation. In International Conference on Learning Representations.

[31]

W Nicholson Price and I Glenn Cohen. 2019. Privacy in the age of medical big data. Nature medicine 25, 1 (2019), 37–43.

[32]

Matthew A Reyna, Chris Josef, Salman Seyedi, Russell Jeter, Supreeth P Shashikumar, M Brandon Westover, Ashish Sharma, Shamim Nemati, and Gari D Clifford. 2019. Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019. In 2019 Computing in Cardiology (CinC). IEEE, Page–1.

[33]

Kristina E Rudd, Sarah Charlotte Johnson, Kareha M Agesa, Katya Anne Shackelford, Derrick Tsoi, Daniel Rhodes Kievlan, Danny V Colombara, Kevin S Ikuta, Niranjan Kissoon, Simon Finfer, 2020. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. The Lancet 395, 10219 (2020), 200–211.

[34]

Supreeth P Shashikumar, Matthew D Stanley, Ismail Sadiq, Qiao Li, Andre Holder, Gari D Clifford, and Shamim Nemati. 2017. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. Journal of electrocardiology 50, 6 (2017), 739–743.

[35]

Mervyn Singer, Clifford S. Deutschman, Christopher Warren Seymour, Manu Shankar-Hari, Djillali Annane, Michael Bauer, Rinaldo Bellomo, Gordon R. Bernard, Jean-Daniel Chiche, Craig M. Coopersmith, Richard S. Hotchkiss, Mitchell M. Levy, John C. Marshall, Greg S. Martin, Steven M. Opal, Gordon D. Rubenfeld, Tom van der Poll, Jean-Louis Vincent, and Derek C. Angus. 2016. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315, 8 (02 2016), 801–810. https://doi.org/10.1001/jama.2016.0287

[36]

Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. 2014. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014).

[37]

Franco van Wyk, Anahita Khojandi, and Rishikesan Kamaleswaran. 2019. Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study. IEEE Journal of Biomedical and Health Informatics 23, 3 (2019), 978–986. https://doi.org/10.1109/JBHI.2019.2894570

[38]

Kuba Weimann and Tim OF Conrad. 2021. Transfer learning for ECG classification. Scientific reports 11, 1 (2021), 1–12.

[39]

Jianfeng Xie, Hongliang Wang, Yan Kang, Lixin Zhou, Zhongmin Liu, Bingyu Qin, Xiaochun Ma, Xiangyuan Cao, Dechang Chen, Weihua Lu, 2020. The epidemiology of sepsis in Chinese ICUs: a national cross-sectional survey. Critical Care Medicine 48, 3 (2020), e209–e218.

[40]

Morteza Zabihi, Serkan Kiranyaz, and Moncef Gabbouj. 2019. Sepsis prediction in intensive care unit using ensemble of XGboost models. In 2019 Computing in Cardiology (CinC). IEEE, Page–1.

[41]

Yu Zhang and Qiang Yang. 2021. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering (2021).

[42]

Ligeng Zhu, Zhijian Liu, and Song Han. 2019. Deep leakage from gradients. Advances in neural information processing systems 32 (2019).

Cited By

Li QMa HSong DBai YZhao LXie K(2024)Early prediction of sepsis using chatGPT-generated summaries and structured dataMultimedia Tools and Applications10.1007/s11042-024-18378-7Online publication date: 9-Feb-2024
https://doi.org/10.1007/s11042-024-18378-7
Chen LWang LXu JChen SWang WZhao WLi QWang LFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614946(234-244)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614946

Index Terms

Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

Recommendations

Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations
Abstract
Sepsis is the third leading cause of death worldwide. Antibiotics are an important component in the treatment of sepsis. The use of antibiotics is currently facing the challenge of increasing antibiotic resistance (Evans et al., 2021). Sepsis ...
Graphical abstract

Display Omitted
Highlights
- Using deep reinforcement learning to provide antibiotic treatment recommendations for sepsis.
- Integrated with clinical data to formulate highly targeted antibiotic combination recommendations.
- Guided by clinical expert knowledge ...
A Cross-Sectional Study to Profile Critical Access Hospitals and Compare Outcomes of Care Against Non-Critical Acute Settings
A Bayesian network for early diagnosis of sepsis patients: a basis for a clinical decision support system
ICCABS '12: Proceedings of the 2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences

Sepsis is a severe medical condition caused by an inordinate immune response to an infection. Early detection of sepsis symptoms is important to prevent the progression into the more severe stages of the disease, which kills one in four it effects. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Artifacts Available / v1.1

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

NSFC
National Key R&D Program of China

Conference

WWW '23

Sponsor:

SIGWEB

WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
117
Total Downloads

Downloads (Last 12 months)78
Downloads (Last 6 weeks)7

Reflects downloads up to 10 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li QMa HSong DBai YZhao LXie K(2024)Early prediction of sepsis using chatGPT-generated summaries and structured dataMultimedia Tools and Applications10.1007/s11042-024-18378-7Online publication date: 9-Feb-2024
https://doi.org/10.1007/s11042-024-18378-7
Chen LWang LXu JChen SWang WZhao WLi QWang LFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614946(234-244)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614946

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents