ICCS-Lab, American University of Culture and Education (AUCE), Beirut 1105, Lebanon.
Lab-STICC, CNRS UMR 6285, Ensta-Bretagne, 29200 Brest, France.
Sensors (Basel). 2020 Mar 30;20(7):1931. doi: 10.3390/s20071931.
Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients' classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.
如今，越来越多的患者出现新的症状和疾病，这使得医疗保健监测和评估成为医务人员和医院的一项复杂任务。事实上，生物医学传感器所收集的大量异构数据的处理，以及患者分类和疾病诊断的需求，给许多基于健康的传感应用带来了重大挑战。因此，远程感应设备和大数据技术的结合已经被证明是医疗保健应用的一种高效、低成本的解决方案。在本文中，我们提出了一个用于实时患者监测和决策制定的稳健大数据分析平台，以帮助医院和医务人员。该平台依赖于大数据技术和数据分析技术，由四个层组成：实时患者监测、实时决策和数据存储、患者分类和疾病诊断，以及数据检索和可视化。为了评估我们平台的性能，我们基于 Hadoop 生态系统实现了我们的平台，并在真实的健康数据上应用了所提出的算法。得到的结果表明，我们的平台在医疗保健应用中高效地进行患者分类和疾病诊断方面具有有效性。
