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A Decision Support System on Artificial Intelligence Based Early Diagnosis of Sepsis

Year 2022, Volume 2, Issue 1, 14 - 26, 30.04.2022


Sepsis is the intense reaction of the immune system as a result of a severe infection in any part of the body and damages to organs and tissues. And this disease is commonly fatal and costly. In this study, we perform a comparative study for Sepsis prediction using machine learning algorithms from original laboratory findings. For this purpose, thirty-two different machine learning algorithms including different tructures as well as neural network classifiers are evaluated and compared. As a result of experimental studies, SVM (Cubic, Fine Gaussian), KNN (Fine, Weighted, Subspace), Trees (Weighted, Boosted, Bagged) and neural network-based classifiers have achieved a significant success rate in the diagnosis of Sepsis using the new dataset. Thus, it is concluded that it is appropriate to use machine learning algorithms to predict whether a Sepsis patient will be survived. This study has the potential to be used as a new supportive tool for doctors when predicting Sepsis.


  • [1] ConseDefinitions fornsfor Sepsis and Septic Shock (Sepsis-3). JAMA 2016 23 Şubat; 315 (8): 801-810.
  • [2] Global Sepsis Alliance Web Site (Last Access: 03, March 2022), priorestimates-children-and-poor-regions-hit-hardest-global-burden-disease-study-kristina-rudd
  • [3] Kaya U, Yilmaz A, Díkmen Y. (2018). Prediction of sepsis disease by Artificial Neural Networks. Journal of Selcuk-Technic.Special Issue 2018 (ICENTE'18):107-31.
  • [4] Gultepe E, Green J, Nguyen H, Adams J, Albertson T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system J Am Med Inform Doç. 2014; 21 (2): 315–25. doi: 10.1136
  • [5] Desautels T, Calvert J, Hoffman J, et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform 2016;4:e28.
  • [6] Fleuren LM, Klausch TLT, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, Swart EL, Girbes ARJ, Thoral P, Ercole A, Hoogendoorn M, Elbers PWG. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy Yoğun Bakım Med. 2020 Mart; 46 (3): 383–400. doi: 10.1007
  • [7] Gültepe E Nguyen H Albertson T et al. A Bayesian network for early diagnosis of sepsis patients: a basis for a clinical decision support system 2. IEEE Uluslararası Biyo ve Tıp Bilimlerinde Hesaplamalı Gelişmeler Konferansı (ICCABS); Las Vegas, NV: 23–25, 2012, 1-5.
  • [8] Stanculescu I, Williams C.K.I, Y. Freer Y. Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis, in IEEE Journal of Biomedical and Health Informatics, Sept. 2014, vol. 18, no. 5, 1560-1570.
  • [9] Guillén J. et al. Predictive models for severe sepsis in adult ICU patients, 2015 Systems and Information Engineering Design Symposium, Charlottesville, VA, 2015, 182-187.
  • [10] [Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R, et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J. Am. Med. Inform. Assoc. (2014) 21:326–36. 10.1136/amiajnl-2013-001854
  • [11] Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE (2017) 12:e0174708. 10.1371/journal.pone.0174708
  • [12] Lamping F, Jack T, Rübsamen N, Sasse M, Beerbaum P, Mikolajczyk RT, et al. Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children – a data-driven approach using machine-learning algorithms. BMC Pediatr. (2018) 18:112.10.1186/s12887-018-1082-2
  • [13] Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU. Pediatr. Crit. Care Med. (2018) 19:e495–503. 10.1097/PCC.0000000000001666
  • [14] Calvert J, Saber N, Hoffman J, Das R. Machine-learning-based laboratory developed test for the diagnosis of sepsis in high-risk patients. Diagnostics. (2019) 9:20. 10.3390/diagnostics9010020
  • [15] Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.
  • [16] [Barton C, Chettipally U, Zhou Y, Jiang Z, Lynn-Palevsky A, Le S, et al. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs. Comput. Biol. Med. (2019) 109:79–84. 10.1016/j.compbiomed.2019.04.027
  • [17] Le S, Hoffman J, Barton C, Fitzgerald JC, Allen A, Pellegrini E, et al. Pediatric severe sepsis prediction using machine learning. Front. Pediatr. (2019) 7:413. 10.3389/fped.2019.00413
  • [18] Yee CR, Narain NR, Akmaev VR, Vamulapalli V. Yoğun bakım ünitesinde septik şoku tahmin etmeye yönelik veriye dayalı bir yaklaşım Biyomedya. Bilgi vermek. Analizler _ (2019) 11 :1178222619885147. 10.1177/1178222619885147
  • [19] Seymour, C. W., Liu, V. X., Iwashyna, T. J., Brunkhorst, F. M., Rea, T. D., Scherag, A., ... & Deutschman, C. S. (2016). Assessment of clinical criteria for sepsis: for the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama, 315(8), 762-774.
  • [20] Centers for Disease Control and Prevention (CDC). Hospital Toolkit for Adult Sepsis Surveillance 2018
  • [21] Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., ... & Osborn, T. M. (2013). Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive care medicine, 39(2)
  • [22] M. Yang, J. A. De Loera, A machine learning approach to evaluate Beijing air quality. 2018.
  • [23] Hal Daumé III 2012, A Course in Machine Learning,, Accessed 22 Sep 2014.
  • [24] M. Mohri, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning, the MIT Press, 2012. ISBN: 0-262-01825-X.
  • [25] E. Alpaydın, Introduction to Machine Learning, MIT Press, 2014. ISBN: 978-0-262-02818-9.
  • [26] P. Harrington, Machine Learning in Action, 1st Edition, Manning Publications Shelter Island, NY, 2012. ISBN: 978-1-61729-018-3.
  • [27] E. Kartal, Sınıflandırmaya dayalı makine öğrenmesi teknikleri ve kardiyolojik risk değerlendirmesine ilişkin bir uygulama, PhD. Thesis, İstanbul University, 2015.
  • [28] Zendehboudi Alireza, Baseer MA, Saidur R. Application of support vector machine models for forecasting solar and wind energy resources: a review. J Clean Prod 2018;199:272–85.
  • [29] Tasçı Erdal, Onan Aytug. ˘ Investigation of the effect of K-nearest neighbor algorithm parameters on classification performance. In: Turkish) akademik bilişim; 2016.
  • [30] R.Gürfidan, M. Ersoy, Classification of Death-Related to Heart Failure by Machine Learning Algorithms, Advances in Artificial Intelligence Research, 1(1) (2021) 13-18.
  • [31] F. Yang, An implementation of naive Bayes classifier,"Computational Science and Computational Intelligence (CSCI) (2018) 301-306.


Primary Language English
Subjects Engineering, Medicine
Journal Section Research Articles

Pınar KAYA AKSOY This is me (Primary Author)

Fatih ERDEMİR This is me
Amatis Software Engineering
The Netherlands

Deniz KILINÇ This is me

Orhan ER This is me

Publication Date April 30, 2022
Published in Issue Year 2022, Volume 2, Issue 1


APA Kaya Aksoy, P. , Erdemir, F. , Kılınç, D. & Er, O. (2022). A Decision Support System on Artificial Intelligence Based Early Diagnosis of Sepsis . Artificial Intelligence Theory and Applications , 2 (1) , 14-26 . Retrieved from