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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2017

Open Access 01-12-2017 | Research article

Supervised learning for infection risk inference using pathology data

Authors: Bernard Hernandez, Pau Herrero, Timothy Miles Rawson, Luke S. P. Moore, Benjamin Evans, Christofer Toumazou, Alison H. Holmes, Pantelis Georgiou

Published in: BMC Medical Informatics and Decision Making | Issue 1/2017

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Abstract

Background

Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions.

Methods

From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using ten-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions.

Results

More than 50% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available.

Conclusion

The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clinical Decision Support Systems could benefit from these findings to assist clinicians in deciding whether or not to initiate antimicrobial therapy to improve prescription practices.
Literature
1.
Wise R, Hart T, Cars O, Streulens M, Helmuth R, Huovinen P, Sprenger M. Antimicrobial resistance is a major threat to public health. Br Med J. 1998; 317(7159):609–11.CrossRef
2.
O’Neill J. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. London: Review on Antimicrobial Resistance. 2014. p. 1–16.
3.
Holmes AH, Moore LS, Sundsfjord A, Steinbakk M, Regmi S, Karkey A, Guerin PJ, Piddock LJ. Understanding the mechanisms and drivers of antimicrobial resistance. Lancet. 2016; 387(10014):176–87.CrossRefPubMed
4.
Banoo S, Bell D, Bossuyt P, Herring A, Mabey D, Poole F, Smith PG, Sriram N, Wongsrichanalai C, Linke R, et al. Evaluation of diagnostic tests for infectious diseases: general principles. Nat Rev Microbiol. 2008; 8:16–28.CrossRef
5.
Byl B, Clevenbergh P, Jacobs F, Struelens MJ, Zech F, Kentos A, Thys JP. Impact of infectious diseases specialists and microbiological data on the appropriateness of antimicrobial therapy for bacteremia. Clin Infect Dis. 1999; 29(1):60–6.CrossRefPubMed
6.
Harbarth S, Garbino J, Pugin J, Romand JA, Lew D, Pittet D. Inappropriate initial antimicrobial therapy and its effect on survival in a clinical trial of immunomodulating therapy for severe sepsis. Am J Med. 2003; 115(7):529–35.CrossRefPubMed
7.
Willemsen I, Groenhuijzen A, Bogaers D, Stuurman A, van Keulen P, Kluytmans J. Appropriateness of antimicrobial therapy measured by repeated prevalence surveys. Antimicrob Agents Chemother. 2007; 51(3):864–7.CrossRefPubMedPubMedCentral
8.
9.
McGregor JC, Weekes E, Forrest GN, Standiford HC, Perencevich EN, Furuno JP, Harris AD. Impact of a computerized clinical decision support system on reducing inappropriate antimicrobial use: a randomized controlled trial. J Am Med Inform Assoc. 2006; 13(4):378–84.CrossRefPubMedPubMedCentral
10.
Paul M, Andreassen S, Nielsen AD, Tacconelli E, Almanasreh N, Fraser A, Yahav D, Ram R, Leibovici L, Group TS, et al. Prediction of bacteremia using treat, a computerized decision-support system. Clin Infect Dis. 2006; 42(9):1274–82.CrossRefPubMed
11.
Mullett CJ, Thomas JG, Smith CL, Sarwari AR, Khakoo RA. Computerized antimicrobial decision support: an offline evaluation of a database-driven empiric antimicrobial guidance program in hospitalized patients with a bloodstream infection. Int J Med Inform. 2004; 73(5):455–60.CrossRefPubMed
12.
Cleophas TJ, Zwinderman AH, Cleophas-Allers HI. Machine Learning in Medicine. Netherlands: Springer; 2013.CrossRef
13.
Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian networks in biomedicine and health-care. Artif Intell Med. 2004; 30(3):201–14.CrossRefPubMed
14.
Negnevitsky M. Artificial Intelligence: a Guide to Intelligent Systems. England: Pearson Education; 2005.
15.
Richardson AM, Hawkins S, Shadabi F, Sharma D, Fulcher J, Lidbury B, et al.Enhanced laboratory diagnosis of human Chlamydia pneumoniae through pattern recognition derived from pathology database analysis. In: Supplementary proceedings of the third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008). Melbourne, Australia: 2008. p. 227–34.
16.
Richardson AM, Lidbury BA. Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data. BMC Bioinformatics. 2013; 14(1):1.CrossRef
17.
Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R, Chen Y, Romano-Keeler J, Nian H, Weitkamp JH. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc. 2014; 21(2):326–36.CrossRefPubMed
18.
19.
Sierra R, Rello J, Bailén MA, Benítez E, Gordillo A, León C, Pedraza S. C-reactive protein used as an early indicator of infection in patients with systemic inflammatory response syndrome. Intensive Care Med. 2004; 30(11):2038–45. doi:10.​1007/​s00134-004-2434-y.CrossRefPubMed
20.
Mohri M, Rostamizadeh A, Talwalkar A. Foundations of Machine Learning. England: MIT press; 2012.
21.
Metsis V, Androutsopoulos I, Paliouras G. Spam filtering with naive bayes-which naive bayes? In: Third Conference on Email and Anti-Spam, CEAS 2006, Mountain View, California, USA: 2006. p. 28–69.
22.
Hernandez B. Multi-View Object Recognition and Classification. Graph-Based Representation of Visual Features and Structured Learning and Prediction. Stockholm, Sweden: KTH, School of Computer Science and Communication (CSC); 2013.
23.
Shin H, Cho S. How to deal with large dataset, class imbalance and binary output in svm based response model. Proceedings of the Korean Data Mining Society Conference. 2003:93–107. Best Paper Award.
24.
Platt J, et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classifiers. 1999; 10(3):61–74.
25.
Johnson AE, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine learning and decision support in critical care. Proc IEEE. 2016; 104(2):444–66.CrossRef
26.
Osborne JW, Overbay A. The power of outliers (and why researchers should always check for them). Pract Assess Res Eval. 2004; 9(6):1–12.
27.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. Smote: synthetic minority over-sampling technique. J Artif Intell Res. 2002; 16:321–57.
28.
Niculescu-Mizil A, Caruana R. Predicting good probabilities with supervised learning. In: Proceedings of the 22nd International Conference on Machine Learning (ICML) 2005, vol. 149. Bonn, Germany: ACM: 2005. p. 625–32.
29.
Bekkar M, Djemaa HK, Alitouche TA. Evaluation measures for models assessment over imbalanced datasets. J Inf Eng Appl.2013;3(10).
30.
Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics. 2000; 16(5):412–24.CrossRefPubMed
31.
He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009; 21(9):1263–84.CrossRef
32.
Fawcett T. An introduction to roc analysis. Pattern Recogn Lett. 2006; 27(8):861–74.CrossRef
33.
Davis J, Goadrich M. The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on Machine learning (ICML) 2006. vol, 148. Pittsburgh, Pennsylvania, USA: ACM: 2006. p. 233–40.
34.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011; 12:2825–30.
35.
Lemaître G, Nogueira F, Aridas CK. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. J Machine Learning Research. 2017; 18:17:1–17:5.
36.
McKinney W. pandas: a foundational python library for data analysis and statistics. Python for High Performance and Scientific Computing. 2011:1–9.
37.
McKinney W, et al. Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference. vol. 445: 2010. p. 51–6.
38.
Hunter JD. Matplotlib: A 2d graphics environment. Comput Sci Eng. 2007; 9(3):90–5.CrossRef
39.
Waskom M, Botvinnik O, drewokane, Hobson P, David, Halchenko Y, Lukauskas S, Cole JB, Warmenhoven J, de Ruiter J, Hoyer S, Vanderplas J, Villalba S, Kunter G, Quintero E, Martin M, Miles A, Meyer K, Augspurger T, Yarkoni T, Bachant P, Williams M, Evans C, Fitzgerald C, Brian, Wehner D, Hitz G, Ziegler E, Qalieh A, Lee A. seaborn: v0.7.1 (June 2016). 2016. doi: doi:10.5281/zenodo.54844.
40.
Leibovici L, Paul M, Nielsen AD, Tacconelli E, Andreassen S. The treat project: decision support and prediction using causal probabilistic networks. Int J Antimicrob Agents. 2007; 30:93–102.CrossRef
41.
Nargis W, Md I, Ahamed BU. Procalcitonin versus C-reactive protein: Usefulness as biomarker of sepsis in ICU patient. International Journal of Critical Illness and Injury Science. 2014; 54(3):195–99.CrossRef
42.
Previsdomini M, Gini M, Cerutti B, Dolina M, Perren A. Predictors of positive blood cultures in critically ill patients: a retrospective evaluation. Croatian Medical Journal. 2012; 53(1):30–9.CrossRefPubMedPubMedCentral
43.
Hernandez B, Herrero P, Rawson TM, Moore LSP, Charani E, Holmes AH, Georgiou P. Data-driven Web-based Intelligent Decision Support System for Infection Management at Point-Of-Care: Case-Based Reasoning Benefits and Limitations. In: Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017). Porto, Portugal: ScitePress: 2017. p. 119–27. doi:10.​5220/​0006148401190127​.
Metadata
Title
Supervised learning for infection risk inference using pathology data
Authors
Bernard Hernandez
Pau Herrero
Timothy Miles Rawson
Luke S. P. Moore
Benjamin Evans
Christofer Toumazou
Alison H. Holmes
Pantelis Georgiou
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2017
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-017-0550-1

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