Mortality Prediction of Older Adults Admitted to the Emergency Department

KIPS Transactions on Software and Data Engineering, Vol. 7, No.7, pp.275-280, July 2018
10.3745/KTSDE.2018.7.7.275, Full Text

Abstract

As the global population becomes aging, the demand for health services for the elderly is expected to increase. In particular, The elderly visiting the emergency department sometimes have complex medical, social, and physical problems, such as having a variety of illnesses or complaints of unusual symptoms. The proposed system is designed to predict the mortality of the elderly patients who are over 65 years old and have admitted the emergency department. For mortality prediction, we compare the support vector machines and Feed Forward Neural Network (FFNN) trained with medical data such as age, sex, blood pressure, body temperature, etc. The results of the FFNN with a hidden layer are best in the mortality prediction, and F1 score and the AUC is 52.0%, 88.6% respectively. If we improve the performance of the proposed system by extracting better medical features, we will be able to provide better medical services through an effective and quick allocation of medical resources for the elderly patients visiting the emergency department.


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Cite this paper

[KIPS Transactions Style]
J. Park and S. Lee, "Mortality Prediction of Older Adults Admitted to the Emergency Department," KIPS Transactions on Software and Data Engineering, Vol.7, No.7, pp.275-280, 2018, DOI: 10.3745/KTSDE.2018.7.7.275.

[IEEE Style]
Junhyeok Park and Songwook Lee, "Mortality Prediction of Older Adults Admitted to the Emergency Department," KIPS Transactions on Software and Data Engineering, vol. 7, no. 7, pp. 275-280, 2018. DOI: 10.3745/KTSDE.2018.7.7.275.

[ACM Style]
Park, J. and Lee, S. 2018. Mortality Prediction of Older Adults Admitted to the Emergency Department. KIPS Transactions on Software and Data Engineering, 7, 7, (2018), 275-280. DOI: 10.3745/KTSDE.2018.7.7.275.