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, Pages 286-291 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2019.64926 | ||
| Authors | ||
| Youssef M. Essa* 1; Ahmed Al-Mahalawy2; Gamal Attiya2; Ayman El-Sayed2 | ||
| 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 | ||
| References | ||
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