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Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

Yazan Alnsour, Rassule Hadidi, Neetu Singh
Copyright: © 2019 |Volume: 14 |Issue: 3 |Pages: 18
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781522564546|DOI: 10.4018/IJHISI.2019070104
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MLA

Alnsour, Yazan, et al. "Using Data Analytics to Predict Hospital Mortality in Sepsis Patients." IJHISI vol.14, no.3 2019: pp.40-57. http://doi.org/10.4018/IJHISI.2019070104

APA

Alnsour, Y., Hadidi, R., & Singh, N. (2019). Using Data Analytics to Predict Hospital Mortality in Sepsis Patients. International Journal of Healthcare Information Systems and Informatics (IJHISI), 14(3), 40-57. http://doi.org/10.4018/IJHISI.2019070104

Chicago

Alnsour, Yazan, Rassule Hadidi, and Neetu Singh. "Using Data Analytics to Predict Hospital Mortality in Sepsis Patients," International Journal of Healthcare Information Systems and Informatics (IJHISI) 14, no.3: 40-57. http://doi.org/10.4018/IJHISI.2019070104

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Abstract

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.