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Early prediction of sepsis using a high-order Markov dynamic Bayesian network (HMDBN) classifier

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Abstract

Sepsis is among the leading causes of morbidity, mortality and high costs in the ICU. The early prediction and intervention of sepsis is a challenging task under strict time and cost constraints. In this paper, a novel High-order Markov Dynamic Bayesian Network (HMDBN) classifier with discrete features is presented for early prediction of sepsis at a high-order time point. The model structure is learned from the unrolled DBN by performing the K2 algorithm, and the features ‘disappeared’ in the prediction are eliminated using the VE method. Based on a few vital signs and laboratory results, an intuitive causal graph and indicating system are constructed to realize continuous prediction and probabilistic interpretation in real-time. Compared with other ten classical machine learning classifiers on evaluation metrics, HMDBN models have the highest AUROC scores on both internal tests and external validations for sepsis early prediction, and provide identifiable and interpretable results that allowing clinicians to immediately understand the reason for the prediction.

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Data availability

The datasets that support the findings of the current study are available from the corresponding author on reasonable request.

References

  1. Hotchkiss R, Moldawer L, Opal S et al (2016) Sepsis and septic shock. Nat Rev Dis Primers 2:16045. https://doi.org/10.1038/nrdp.2016.45

    Article  Google Scholar 

  2. Rello J, Valenzuela-Sánchez F, Ruiz-Rodriguez M, Moyano S (2017) Sepsis: A Review of Advances in Management. Adv Ther 34:2393–2411. https://doi.org/10.1007/s12325-017-0622-8

    Article  Google Scholar 

  3. Fleischmann-Struzek C, Goldfarb DM, Schlattmann P, Schlapbach LJ, Reinhart K, Kissoon N (2018) The global burden of paediatric and neonatal sepsis: a systematic review. Lancet Respir Med 6(3):223–230. https://doi.org/10.1016/S2213-2600(18)30063-8

    Article  Google Scholar 

  4. Dai T, Tayur S (2018) Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, 1st edn. Wiley, New York, p 414

    Book  Google Scholar 

  5. Subbe CP, Slater A, Menon D, Gemmell L (2006) Validation of physiological scoring systems in the accident and emergency department. Emerg Med J 23:841–845. https://doi.org/10.1136/emj.2006.035816

    Article  Google Scholar 

  6. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M et al (2016) The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315(8):801–810. https://doi.org/10.1001/jama.2016.0287

    Article  Google Scholar 

  7. Qin Q, Xia YQ, Cao Y (2017) Clinical study of a new Modified Early Warning System scoring system for rapidly evaluating shock in adults. J Crit Care 37:50–55. https://doi.org/10.1016/j.jcrc.2016.08.025

    Article  Google Scholar 

  8. Sande DVD, Genderen MEV, Huiskens J, Gommers D, Bommel JV (2021) Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med 47:750–760. https://doi.org/10.1007/s00134-021-06446-7

    Article  Google Scholar 

  9. Zohar Y, Itskovich SZ, Koren S, Zaidenstein R, Marchaim D, Koren R (2021) The association of diabetes and hyperglycemia with sepsis outcomes: a population-based cohort analysis. Intern Emerg Med 16:719–728. https://doi.org/10.1007/s11739-020-02507-9

    Article  Google Scholar 

  10. Lai H, Wu G, Zhong Y, Chen G, Zhang W, Shi S, Xia Z (2023) Red blood cell distribution width improves the prediction of 28-day mortality for patients with sepsis-induced acute kidney injury: A retrospective analysis from MIMIC-IV database using propensity score matching. J Intensive Med 3:275–282. https://doi.org/10.1016/j.jointm.2023.02.005

    Article  Google Scholar 

  11. Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R (2016) A computational approach to early sepsis detection. Comput Biol Med 74:69–73. https://doi.org/10.1016/j.compbiomed.2016.05.003

    Article  Google Scholar 

  12. Harutyunyan H, Khachatrian H, Kale DC, Steeg GV, Galstyan A (2019) Multitask learning and benchmarking with clinical time series data. Sci Data 6(96). https://doi.org/10.1038/s41597-019-0103-9

  13. Fagerstrom J, Bang M, Wilhelms D, Chew MS (2019) LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock. Sci Rep 9:15132. https://doi.org/10.1038/s41598-019-51219-4

    Article  Google Scholar 

  14. Scherpf M, Gräßer F, Malberg H, Zaunseder S (2019) Predicting sepsis with a recurrent neural network using the MIMIC III database. Comput Biol Med 113:103395. https://doi.org/10.1016/j.compbiomed.2019.103395

    Article  Google Scholar 

  15. Shashikumar SP, Josef CS, Sharma A, Nemati S (2021) DeepAISE - An interpretable and recurrent neural survival model for early prediction of sepsis. Artif Intell Med 113:102036. https://doi.org/10.1016/j.artmed.2021.102036

    Article  Google Scholar 

  16. Kaji DA, Zech JR, Kim JS, Cho SK, Dangayach NS, Costa AB, Oermann EK (2019) An attention based deep learning model of clinical events in the intensive care unit. PLoS One 14(2):e0211057. https://doi.org/10.1371/journal.pone.0211057

    Article  Google Scholar 

  17. Javan SL, Sepehri MM, Javan ML, Khatibi T (2019) An intelligent warning model for early prediction of cardiac arrest in sepsis patients. Comput Methods Prog Biomed 178:47–58. https://doi.org/10.1016/j.cmpb.2019.06.010

    Article  Google Scholar 

  18. Duan Y, Huo J, Chen M, Hou F, Yan G, Li S, Wang H (2023) Early prediction of sepsis using double fusion of deep features and handcrafted features. Appl Intell 53:17903–17919. https://doi.org/10.1007/s10489-022-04425-z

    Article  Google Scholar 

  19. Friedman N, Murphy K, Russell S (1998) Learning the structure of dynamic probabilistic networks. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998). https://doi.org/10.48550/arXiv.1301.7374

  20. Murphy K, Russell S (2002) Dynamic Bayesian Networks: Representation, Inference and Learning. Dissertation, University of California, Berkeley

  21. Friedman N (1997) Learning Belief Networks in the Presence of Missing Values and Hidden Variables. Int Conf Mach Learn ICML-97:125–133. https://doi.org/10.5555/645526.657145

    Article  Google Scholar 

  22. Peelen L, Keizer NF, Jonge E, Bosman RJ, Abu-Hanna A, Peek N (2010) Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit. J Biomed Inform 43(2):273–286. https://doi.org/10.1016/j.jbi.2009.10.002

    Article  Google Scholar 

  23. Friedman N (2013) The Bayesian Structural EM Algorithm. The Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998). https://doi.org/10.48550/arXiv.1301.7373

  24. Dabrowski JJ, Beyers C, Villiers JP (2016) Systemic banking crisis early warning systems using dynamic Bayesian networks. Expert Syst Appl 62:225–242. https://doi.org/10.1016/j.eswa.2016.06.024

    Article  Google Scholar 

  25. Nachimuthu SK, Haug PJ (2012) Early detection of sepsis in the emergency department using Dynamic Bayesian Networks. AMIA Ann Symp Proc 2012:653–662. https://www.researchgate.net/publication/234099960. Accessed 2022-12-08

    Google Scholar 

  26. Johnson A, Pollard TJ, Shen L et al (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3:160035. https://doi.org/10.1038/sdata.2016.35

    Article  Google Scholar 

  27. Griffin JE, Łatuszyński KG, Steel MF (2021) In search of lost mixing time: adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p. Biometrika 108(1):53–69. https://doi.org/10.1093/biomet/asaa055

    Article  MathSciNet  MATH  Google Scholar 

  28. Kumari T, Guleria V, Syal P, Aggarwal AK (2021) A Feature Cum Intensity Based SSIM Optimised Hybrid Image Registration Technique. 2021 International Conference on Computing, Communication and Green Engineering (CCGE), IEEE. https://doi.org/10.1109/CCGE50943.2021.9776407

  29. Levy MM, Fink MP, Marshall JC et al (2003) 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Intensive Care Med 29:530–538. https://doi.org/10.1007/s00134-003-1662-x

    Article  Google Scholar 

  30. Shavdia D (2007) Septic shock: providing early warnings through multivariate logistic regression models. Dissertation, Harvard University, MIT Division of Health Sciences and Technology. https://dspace.mit.edu/handle/1721.1/42338. Accessed 27 Oct 2021

  31. Schamoni S, Lindner HA, Schneider-Lindner V, Thiel M, Riezler S (2019) Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction. Artif Intell Med 100:101725. https://doi.org/10.1016/j.artmed.2019.101725

    Article  Google Scholar 

  32. Elreedy D, Atiya AF (2019) A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance. Inf Sci 505:32–64. https://doi.org/10.1016/j.ins.2019.07.070

    Article  Google Scholar 

  33. Koller D, Friedman N (2009) Probabilistic Graphical Models: Principles and Techniques, 1st edn. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  34. Cooper GF, Herskovits E (1992) A Bayesian Method for the Induction of Probabilistic Networks from Data. Mach Learn 9:309–347. https://doi.org/10.1007/BF00994110

    Article  MATH  Google Scholar 

  35. Wang S, Zhang S, Wu T, Duan Y, Zhou L, Lei H (2020) FMDBN: A first-order Markov dynamic Bayesian network classifier with continuous attributes. Knowl-Based Syst 195:105638. https://doi.org/10.1016/j.knosys.2020.105638

    Article  Google Scholar 

  36. Ye Y, Li L, Lin Q, Wong KC, Li J, Ming Z (2022) Knowledge guided Bayesian classification for dynamic multi-objective optimization. Knowl-Based Syst 250:109173. https://doi.org/10.1016/j.knosys.2022.109173

    Article  Google Scholar 

  37. Miller A, Panneerselvam J, Liu L (2022) A review of regression and classification techniques for analysis of common and rare variants and gene-environmental factors. Neurocomputing 489:466–485. https://doi.org/10.1016/j.neucom.2021.08.150

    Article  Google Scholar 

  38. Lu D, Yue Y, Hu Z, Xu M, Tong Y, Ma H (2023) Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm. Comput Biol Med 159:106930. https://doi.org/10.1016/j.compbiomed.2023.106930

    Article  Google Scholar 

  39. Breiman L, Friedman J, Stone CJ, Olshen RA (2017) Classification and Regression Trees, 1st edn. CRC press, New York

    Book  MATH  Google Scholar 

  40. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  41. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  42. Shahraki A, Abbasi M, Haugen Ø (2020) Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost. Eng Appl Artif Intell 94:103770. https://doi.org/10.1016/j.engappai.2020.103770

    Article  Google Scholar 

  43. Ghosh A, SahaRay R, Chakrabarty S, Bhadra S (2021) Robust generalised quadratic discriminant analysis. Pattern Recogn 117:107981. https://doi.org/10.1016/j.patcog.2021.107981

    Article  Google Scholar 

  44. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  45. Xiao J, Suab SA, Chen X, Singh CK, Singh D, Aggarwal AK et al (2023) Enhancing assessment of corn growth performance using unmanned aerial vehicles (UAVs) and deep learning. Measurement 214:112764. https://doi.org/10.1016/j.measurement.2023.112764

    Article  Google Scholar 

  46. Wang S, Zhang S, Wu T, Duan Y, Zhou L (2022) Research on a dynamic full Bayesian classifier for time-series data with insufficient information. Appl Intell 52:1059–1075. https://doi.org/10.1007/s10489-021-02448-6

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China [Grant numbers 72171176, 72021002, 82072228].

Author information

Authors and Affiliations

  1. School of Economics and Management, Tongji University, Shanghai, 200092, China

    Siwen Zhang & Yongrui Duan

  2. Faculty of Life Sciences and Medicine, King’s College London, London, UK

    Siwen Zhang

  3. Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China

    Fenggang Hou

  4. Department of Intensive Care Medicine, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China

    Guoliang Yan & Haihui Wang

  5. Emergency Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Shufang Li

  6. Jiading District Central Hospital Affiliated to Shanghai University of Medicine & Health Sciences, Shanghai, China

    Liang Zhou

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  1. Siwen Zhang
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  7. Liang Zhou
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Contributions

Siwen Zhang and Yongrui Duan conceived and designed this study. Material preparation, data collection and analysis were performed by Siwen Zhang, Yongrui Duan, Fenggang Hou, Guoliang Yan, Shufang Li, Haihui Wang and Liang Zhou. The first draft of the manuscript was written by Siwen Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yongrui Duan.

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Zhang, S., Duan, Y., Hou, F. et al. Early prediction of sepsis using a high-order Markov dynamic Bayesian network (HMDBN) classifier. Appl Intell 53, 26384–26399 (2023). https://doi.org/10.1007/s10489-023-04920-x

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