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

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Smart Health (ICSH 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10347))

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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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Acknowledgement

This work is supported in part by the Hong Kong Research Grants Council (PolyU 152040/16E), the Hong Kong Polytechnic University (G-UC93, G-YBKX) and the YC Yu Scholarship for Centre for Smart Health.

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Authors and Affiliations

  1. Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China

    Yang Liu

  2. Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China

    Kup-Sze Choi

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  1. Yang Liu
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Correspondence to Kup-Sze Choi .

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Editors and Affiliations

  1. University of Arizona, Tucson, Arizona, USA

    Hsinchun Chen

  2. University of Arizona, Tucson, Arizona, USA

    Daniel Dajun Zeng

  3. Department of Management Information System, University of Georgia, Athens, Georgia, USA

    Elena Karahanna

  4. University of Texas, Richardson, Texas, USA

    Indranil Bardhan

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Liu, Y., Choi, KS. (2017). Using Machine Learning to Diagnose Bacterial Sepsis in the Critically Ill Patients. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-67964-8_22

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