Feature Engineering For Readmission Prediction Model of Real-Time Patient Streaming Data

Essa, Youssef M.; Al-Mahalawy, Ahmed; Attiya, Gamal; El-Sayed, Ayman

doi:10.21608/mjeer.2019.64926

	Feature Engineering For Readmission Prediction Model of Real-Time Patient Streaming Data
Menoufia Journal of Electronic Engineering Research
Article 7, Volume 28, ICEEM2019-Special Issue, 2019, Page 286-291
Document Type: Original Article
DOI: 10.21608/mjeer.2019.64926
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Authors
Youssef M. Essa^* ¹; Ahmed Al-Mahalawy²; Gamal Attiya ²; Ayman El-Sayed ²
¹Senior BigData Engineer Idealo Berlin, Germany
²Computer Science & Engineering Dept. Faculty of Electronic Engineering, Menoufia University Menoufia, Egypt
Abstract
Providing healthcare services without emergency waiting is the one of important challenges for healthcare organizations. The poor patient readmission management increasing the emergency waiting and maybe cause a risk on the patient life. The current prediction models working on batching processing model, and not provides real time tracking of patient status. However, the patient profile growth every second by new records or new attributes and the accuracy of analysis is insufficient when the quality of health data is incomplete, old, or not clean. Indeed, all patient data need to analyze using big data technologies in real time to extract important features from the data. So, this paper tackles most problems that hinder extracting features for readmission prediction models in real time. The new model called High-Risk Readmission Prediction model (HR2P). This model based on machine learning and big data technology to be able streaming patient data from Internet of Things (IoT) and electronic health records (EHR) storage. The new approach allows healthcare organizations to minimize waiting time for patients and emergency cases.
Keywords
Powerset; Distributed Systems; Hadoop; Spark; Big Data


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