Abstract
Computing Mortality for ICU patients, who are in critical conditions and in need of extra intensive care has been a major problem. The focus of this work is to predict patient’s health mortality through health record data from ICU Mortality Prediction Challenged. Data are taken from the first 24 h to figure out the in-hospital death by using few models from machine learning. Here, in this health record-based work, personal health information particularly for ICU patients are recorded and observed by the physicians. These methods are cost-effective, reliable, easily accessible, and are maintained in a Cloud platform to increase the quality of service. We have taken 6 general descriptors recorded at the time of admission to a particular unit ward and other different time-series measurements collected during the first 24 h. This chapter focuses on predicting the mortality of ICU patients by checking their health-care data. We have used online mode that can be access by the physicians, patients, and other staff members easily. Therefore, it has the considerable potential to provide an accurate result with a simple and easily accessible mode. As there is less available research works on ICU patients with Cloud Computing. That’s why, our approach has the potential to reach the prediction of mortality for in-hospital ICU patients using machine learning.
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Ningombam, S., Lodh, S., Majumder, S. (2021). Computing Mortality for ICU Patients Using Cloud Based Data. In: Bansal, J.C., Fung, L.C.C., Simic, M., Ghosh, A. (eds) Advances in Applications of Data-Driven Computing. Advances in Intelligent Systems and Computing, vol 1319. Springer, Singapore. https://doi.org/10.1007/978-981-33-6919-1_11
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