IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0224502.html
   My bibliography  Save this article

Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

Author

Listed:
  • Joon-myoung Kwon
  • Ki-Hyun Jeon
  • Hyue Mee Kim
  • Min Jeong Kim
  • Sungmin Lim
  • Kyung-Hee Kim
  • Pil Sang Song
  • Jinsik Park
  • Rak Kyeong Choi
  • Byung-Hee Oh

Abstract

Objective: Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI). Methods: The data of 22,875 AMI patients from the Korean working group of the myocardial infarction (KorMI) registry were exclusively divided into 12,152 derivation data of 36 hospitals and 10,723 validation data of 23 hospitals. The predictor variables were the initial demographic and laboratory data. The endpoints were in-hospital mortality and 12-months mortality. We compared the DAMI performance with the global registry of acute coronary event (GRACE) score, acute coronary treatment and intervention outcomes network (ACTION) score, and the thrombolysis in myocardial infarction (TIMI) score using the validation data. Results: In-hospital mortality for the study subjects was 4.4% and 6-month mortality after survival upon discharge was 2.2%. The areas under the receiver operating characteristic curves (AUCs) of the DAMI were 0.905 [95% confidence interval 0.902–0.909] and 0.870 [0.865–0.876] for the ST elevation myocardial infarction (STEMI) and non ST elevation myocardial infarction (NSTEMI) patients, respectively; these results significantly outperformed those of the GRACE (0.851 [0.846–0.856], 0.810 [0.803–0.819]), ACTION (0.852 [0.847–0.857], 0.806 [0.799–0.814] and TIMI score (0.781 [0.775–0.787], 0.593[0.585–0.603]). DAMI predicted 30.9% of patients more accurately than the GRACE score. As secondary outcome, during the 6-month follow-up, the high risk group, defined by the DAMI, has a significantly higher mortality rate than the low risk group (17.1% vs. 0.5%, p

Suggested Citation

  • Joon-myoung Kwon & Ki-Hyun Jeon & Hyue Mee Kim & Min Jeong Kim & Sungmin Lim & Kyung-Hee Kim & Pil Sang Song & Jinsik Park & Rak Kyeong Choi & Byung-Hee Oh, 2019. "Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-15, October.
    • Handle: RePEc:plo:pone00:0224502
    DOI: 10.1371/journal.pone.0224502
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0224502
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0224502&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0224502?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Calcagno, Vincent & de Mazancourt, Claire, 2010. "glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i12).
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bernard W T Coetzee & Kevin J Gaston & Steven L Chown, 2014. "Local Scale Comparisons of Biodiversity as a Test for Global Protected Area Ecological Performance: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-11, August.
    2. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    4. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
    5. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    6. Sebastian Gehrmann & Franck Dernoncourt & Yeran Li & Eric T Carlson & Joy T Wu & Jonathan Welt & John Foote Jr. & Edward T Moseley & David W Grant & Patrick D Tyler & Leo A Celi, 2018. "Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.
    7. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).
    8. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    9. Jungyoon Kim & Jihye Lim, 2021. "A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    10. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    11. Yue Sun & Songmin Dai & Jide Li & Yin Zhang & Xiaoqiang Li, 2019. "Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps," Future Internet, MDPI, vol. 11(2), pages 1-12, February.
    12. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    13. Wenjuan Fan & Jingnan Liu & Shuwan Zhu & Panos M. Pardalos, 2020. "Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)," Annals of Operations Research, Springer, vol. 294(1), pages 567-592, November.
    14. Young Jae Kim & Seung Seog Han & Hee Joo Yang & Sung Eun Chang, 2020. "Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-9, June.
    15. Claus Zippel & Sabine Bohnet-Joschko, 2021. "Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    16. Dario Sipari & Betsy D. M. Chaparro-Rico & Daniele Cafolla, 2022. "SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    17. László Kovács, 2019. "Applications of Metaheuristics in Insurance," Society and Economy, Akadémiai Kiadó, Hungary, vol. 41(3), pages 371-395, September.
    18. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    19. Verónica Lloréns-Rico & Ann C. Gregory & Johan Van Weyenbergh & Sander Jansen & Tina Van Buyten & Junbin Qian & Marcos Braz & Soraya Maria Menezes & Pierre Van Mol & Lore Vanderbeke & Christophe Dooms, 2021. "Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    20. Mara Giavina-Bianchi & Raquel Machado de Sousa & Vitor Zago de Almeida Paciello & William Gois Vitor & Aline Lissa Okita & Renata Prôa & Gian Lucca dos Santos Severino & Anderson Alves Schinaid & Rafa, 2021. "Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-13, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0224502. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.