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2017 | OriginalPaper | Chapter

Using Machine Learning to Diagnose Bacterial Sepsis in the Critically Ill Patients

Authors : Yang Liu, Kup-Sze Choi

Published in: Smart Health

Publisher: Springer International Publishing

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Abstract

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Early antibiotic therapy to patients with sepsis is necessary. Every hour of therapy delay could reduce the survival chance of patients with severe sepsis by 7.6%. Certain biomarkers like blood routine and C-reactive protein (CRP) are not sufficient to diagnose bacterial sepsis, and their sensitivity and specificity are relatively low. Procalcitonin (PCT) is the best diagnostic biomarker for sepsis so far, but is still not effective when sepsis occurs with some complications. Machine learning techniques were thus proposed to support diagnosis in this paper. A backpropagation artificial neural network (ANN) classifier, a support vector machine (SVM) classifier and a random forest (RF) classifier were trained and tested using the electronic health record (EHR) data of 185 critically ill patients. The area under curve (AUC), accuracy, sensitivity, and specificity of the ANN, SVM, and RF classifiers were (0.931, 90.8%, 90.2%, 91.6%), (0.940, 88.6%, 92.2%, 84.3%) and (0.953, 89.2%, 88.2%, 90.4%) respectively, which outperformed PCT where the corresponding values were (0.896, 0.716, 0.952, 0.822). In conclusion, the ANN and SVM classifiers explored have better diagnostic value on bacterial sepsis than any single biomarkers involve in this study.

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Singer, M., Deutschman, C.S., Seymour, C., et al.: The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315, 801–810 (2016). doi:10.1001/jama.2016.0287 CrossRef

Angus, D.C., Linde-Zwirble, W.T., Lidicker, J., Clermont, G., Carcillo, J., Pinsky, M.R.: Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit. Care Med. Baltim. 29, 1303–1310 (2001)CrossRef

Liu, V., Escobar, G.J., Greene, J.D., Soule, J., Whippy, A., Angus, D.C., Iwashyna, T.J.: Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA 312, 90–92 (2014)CrossRef

Bone, R.C., Balk, R.A., Cerra, F.B., Dellinger, R.P., Fein, A.M., Knaus, W.A., Schein, R., Sibbald, W.J.: Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest J. 101, 1644–1655 (1992)CrossRef

Sprung, C.L., Sakr, Y., Vincent, J.-L., Le Gall, J.-R., Reinhart, K., Ranieri, V.M., Gerlach, H., Fielden, J., Groba, C.B., Payen, D.: An evaluation of systemic inflammatory response syndrome signs in the sepsis occurrence in acutely ill patients (SOAP) study. Intensive Care Med. 32, 421–427 (2006)CrossRef

Vincent, J.-L., Opal, S.M., Marshall, J.C., Tracey, K.J.: Sepsis definitions: time for change. Lancet Lond. Engl. 381, 774 (2013)CrossRef

Seymour, C.W., Liu, V.X., Iwashyna, T.J., Brunkhorst, F.M., Rea, T.D., Scherag, A., Rubenfeld, G., Kahn, J.M., Shankar-Hari, M., Singer, M.: Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315, 762–774 (2016)CrossRef

Dellinger, R.P., Levy, M.M., Rhodes, A., Annane, D., Gerlach, H., Opal, S.M., Sevransky, J.E., Sprung, C.L., Douglas, I.S., Jaeschke, R.: Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 39, 165–228 (2013)CrossRef

van Zanten, A.R.: The golden hour of antibiotic administration in severe sepsis: avoid a false start striving for gold*. Crit. Care Med. 42, 1931–1932 (2014)CrossRef

10.

Gross, P.A., Patel, B.: Reducing antibiotic overuse: a call for a national performance measure for not treating asymptomatic bacteriuria. Clin. Infect. Dis. 45, 1335–1337 (2007)CrossRef

11.

Harbarth, S., Holeckova, K., Froidevaux, C., Pittet, D., Ricou, B., Grau, G.E., Vadas, L., Pugin, J.: Diagnostic value of procalcitonin, interleukin-6, and interleukin-8 in critically ill patients admitted with suspected sepsis. Am. J. Respir. Crit. Care Med. 164, 396–402 (2001)CrossRef

12.

Miglietta, F., Faneschi, M., Lobreglio, G., Palumbo, C., Rizzo, A., Cucurachi, M., Portaccio, G., Guerra, F., Pizzolante, M.: Procalcitonin, C-reactive protein and serum lactate dehydrogenase in the diagnosis of bacterial sepsis, SIRS and systemic candidiasis. Infez. Med. Riv. Period. Eziologia Epidemiol. Diagn. Clin. E Ter. Delle Patol. Infett. 23, 230–237 (2015)

13.

Wang, K., Bhandari, V., Chepustanova, S., Huber, G., Stephen, O., Corey, S., Shattuck, M.D., Kirby, M.: Which biomarkers reveal neonatal sepsis? PLoS ONE 8, e82700 (2013)CrossRef

14.

Becchi, C., Al Malyan, M., Fabbri, L., Marsili, M., Boddi, V., Boncinelli, S.: Mean platelet volume trend in sepsis: is it a useful parameter? Minerva Anestesiol. 72, 749–756 (2006)

15.

Guven, H., Altintop, L., Baydin, A., Esen, S., Aygun, D., Hokelek, M., Doganay, Z., Bek, Y.: Diagnostic value of procalcitonin levels as an early indicator of sepsis. Am. J. Emerg. Med. 20, 202–206 (2002). doi:10.1053/ajem.2002.33005 CrossRef

16.

Charles, P.E., Ladoire, S., Aho, S., Quenot, J.-P., Doise, J.-M., Prin, S., Olsson, N.-O., Blettery, B.: Serum procalcitonin elevation in critically ill patients at the onset of bacteremia caused by either Gram negative or Gram positive bacteria. BMC Infect. Dis. 8, 1 (2008)CrossRef

17.

Meisner, M., Tschaikowsky, K., Hutzler, A., Schüttler, J., Schick, C.: Postoperative plasma concentrations of procalcitonin after different types of surgery. Intensive Care Med. 24, 680–684 (1998). doi:10.1007/s001340050644 CrossRef

18.

Mimoz, O., Edouard, A.R., Samii, K., Benoist, J.F., Assicot, M., Bohuon, C.: Procalcitonin and C-reactive protein during the early posttraumatic systemic inflammatory response syndrome. Intensive Care Med. 24, 185–188 (1998). doi:10.1007/s001340050543 CrossRef

19.

Hausfater, P., Hurtado, M., Pease, S., Juillien, G., Lvovschi, V.-E., Salehabadi, S., Lidove, O., Wolff, M., Bernard, M., Chollet-Martin, S., Riou, B.: Is procalcitonin a marker of critical illness in heatstroke? Intensive Care Med. 34, 1377–1383 (2008). doi:10.1007/s00134-008-1083-y CrossRef

20.

Mann, E.A., Wood, G.L., Wade, C.E.: Use of procalcitonin for the detection of sepsis in the critically ill burn patient: a systematic review of the literature. Burns. 37, 549–558 (2011). doi:10.1016/j.burns.2010.04.013 CrossRef

21.

Rau, B.M., Kemppainen, E.A., Gumbs, A.A., Büchler, M.W., Wegscheider, K., Bassi, C., Puolakkainen, P.A., Beger, H.G.: Early assessment of pancreatic infections and overall prognosis in severe acute pancreatitis by procalcitonin (PCT): a prospective international multicenter study. Ann. Surg. 245, 745 (2007). doi:10.1097/01.sla.0000252443.22360.46 CrossRef

22.

Kim, Y.J., Kang, S.W., Lee, J.H., Cho, J.H.: Marked elevation of procalcitonin level can lead to a misdiagnosis of anaphylactic shock as septic shock. Int. J. Infect. Dis. 37, 93–94 (2015). doi:10.1016/j.ijid.2015.06.012 CrossRef

23.

Geppert, A., Steiner, A., Delle-Karth, G., Heinz, G., Huber, K.: Usefulness of procalcitonin for diagnosing complicating sepsis in patients with cardiogenic shock. Intensive Care Med. 29, 1384–1389 (2003). doi:10.1007/s00134-003-1827-7 CrossRef

24.

Patil, S., Henry, J.W., Rubenfire, M., Stein, P.D.: Neural network in the clinical diagnosis of acute pulmonary embolism. CHEST J. 104, 1685–1689 (1993)CrossRef

25.

Tu, J.V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49, 1225–1231 (1996)CrossRef

26.

Lammers, R.L., Hudson, D.L., Seaman, M.E.: Prediction of traumatic wound infection with a neural network-derived decision model. Am. J. Emerg. Med. 21, 1–7 (2003). doi:10.1053/ajem.2003.50026 CrossRef

27.

Heckerling, P.S., Canaris, G.J., Flach, S.D., Tape, T.G., Wigton, R.S., Gerber, B.S.: Predictors of urinary tract infection based on artificial neural networks and genetic algorithms. Int. J. Med. Inf. 76, 289–296 (2007). doi:10.1016/j.ijmedinf.2006.01.005 CrossRef

28.

Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). doi:10.1023/A:1022627411411 MATH

29.

Abeel, T., Helleputte, T., Van de Peer, Y., Dupont, P., Saeys, Y.: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26, 392–398 (2010). doi:10.1093/bioinformatics/btp630 CrossRef

30.

Liu, J.J., Cutler, G., Li, W., Pan, Z., Peng, S., Hoey, T., Chen, L., Ling, X.B.: Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 21, 2691–2697 (2005). doi:10.1093/bioinformatics/bti419 CrossRef

31.

Wang, G., Lam, K.-M., Deng, Z., Choi, K.-S.: Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques. Comput. Biol. Med. 63, 124–132 (2015). doi:10.1016/j.compbiomed.2015.05.015 CrossRef

32.

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

33.

Hsieh, C.-H., Lu, R.-H., Lee, N.-H., Chiu, W.-T., Hsu, M.-H., Li (Jack), Y.-C.: Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. Surgery 149, 87–93 (2011). doi:10.1016/j.surg.2010.03.023 CrossRef

34.

Özçift, A.: Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis. Comput. Biol. Med. 41, 265–271 (2011). doi:10.1016/j.compbiomed.2011.03.001 CrossRef

35.

Nguyen, C., Wang, Y., Nguyen, H.N.: Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic (2013). doi:10.4236/jbise.2013.65070

36.

Azar, A.T., Elshazly, H.I., Hassanien, A.E., Elkorany, A.M.: A random forest classifier for lymph diseases. Comput. Methods Programs Biomed. 113, 465–473 (2014). doi:10.1016/j.cmpb.2013.11.004 CrossRef

37.

Lawrence, D.: Health insurance portability and accountability act (HIPAA) privacy rule and the national instant criminal background check system (NICS). Final rule. Fed. Regist. 81, 382–396 (2016)

38.

Nissen, S.: Implementation of a fast artificial neural network library (FANN). Rep. Dep. Comput. Sci. Univ. Cph. DIKU. 31, 29 (2003)

39.

Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). doi:10.1145/1961189.1961199

40.

Google Code Archive - Long-term storage for Google Code Project Hosting. https://code.google.com/archive/p/randomforest-matlab/

41.

Liaw, A., Wiener, M.: Classification and regression by random forest. R. News 2, 18–22 (2002)

42.

Youden, W.J.: Index for rating diagnostic tests. Cancer 3, 32–35 (1950)CrossRef

43.

Miotto, R., Li, L., Kidd, B.A., Dudley, J.T.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016). doi:10.1038/srep26094 CrossRef

44.

Nguyen, P., Tran, T., Wickramasinghe, N., Venkatesh, S.: Deepr: a convolutional net for medical records (2016). ArXiv160707519 Cs Stat

Title: Using Machine Learning to Diagnose Bacterial Sepsis in the Critically Ill Patients
Authors: Yang Liu
Kup-Sze Choi
Publisher: Springer International Publishing
Book: Smart Health
Print ISBN: 978-3-319-67963-1

Electronic ISBN: 978-3-319-67964-8

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-67964-8_22

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