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Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine

Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine

João M. C. Gonçalves, Filipe Portela, Manuel F. Santos, Álvaro Silva, José Machado, António Abelha, Fernando Rua
Copyright: © 2014 |Volume: 9 |Issue: 3 |Pages: 19
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781466654617|DOI: 10.4018/ijhisi.2014070103
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MLA

Gonçalves, João M. C., et al. "Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine." IJHISI vol.9, no.3 2014: pp.36-54. http://doi.org/10.4018/ijhisi.2014070103

APA

Gonçalves, J. M., Portela, F., Santos, M. F., Silva, Á., Machado, J., Abelha, A., & Rua, F. (2014). Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine. International Journal of Healthcare Information Systems and Informatics (IJHISI), 9(3), 36-54. http://doi.org/10.4018/ijhisi.2014070103

Chicago

Gonçalves, João M. C., et al. "Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine," International Journal of Healthcare Information Systems and Informatics (IJHISI) 9, no.3: 36-54. http://doi.org/10.4018/ijhisi.2014070103

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Abstract

Optimal treatments for patients with microbiological problems depend significantly on the ability of the attending physicians to predict sepsis level. A set of Data Mining (DM) models has been developed using forecasting techniques and classification models to aid decision making by physicians about the appropriate, and most effective, therapeutic plan to adopt in specific situations. A combination of Decision Trees, Support Vector Machines and Naïve Bayes classifier were being used to generate the DM models. Confusion Matrix, including associated metrics, and Cross-validation were used to evaluate the models. Associated metrics used to identify the most relevant measures to predict sepsis level and treatment procedures include the analysis of the total error rate, sensitivity, specificity, and accuracy measures. The data used in DM models were collected at the Intensive Care Unit of the Centro Hospitalar do Porto, in Oporto, Portugal. Encapsulated within a supervised learning context, classification models were applied to predict sepsis level and direct the therapeutic plan for patients with sepsis. This work concludes that it was possible to predict sepsis level (2nd and 3rd) with great accuracy (accuracy: 100%), but not for the therapeutic plan (best accuracy level: 62.8%).

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