Title: Parallel data processing approaches for effective intensive care units with internet of things
Authors: Narayanan Manikandan; Srinivasan Subha
Addresses: School of Information Technology and Engineering, VIT University, Vellore, Tamilnadu-632014, India ' School of Information Technology and Engineering, VIT University, Vellore, Tamilnadu-632014, India
Abstract: Computerisation in healthcare is more general and monitoring intensive care units (ICUs) is more significant and life critical. Accurate monitoring in ICU is essential. Failing to take the right decision at the right time may prove fatal. Similarly, a timely decision can save people's lives in various critical situations. In order to increase the accuracy and timeliness in ICU monitoring, two major technologies can be used, namely parallel processing through vectorisation of ICU data and data communication through internet of things (IoT). With our approach, we can improve efficiency and accuracy in data processing. This paper proposes a parallel decision tree algorithm in ICU data to take faster and accurate decisions on data selection. The uses of parallelised algorithms optimise the process of collecting a large set of patient information. Decision tree algorithm is used for examining and extracting knowledge-based data from large databases. Finalised information will be transferred to concerned medical experts in case of medical emergency using IoT. Parallel implementation of decision tree algorithm is implemented with threads and output data is stored in local IoT table for further processing.
Keywords: medical data processing; internet of things; IoT; ICU data; vectorisation; multicore architecture; parallel data processing.
International Journal of Computational Science and Engineering, 2019 Vol.19 No.4, pp.474 - 482
Available online: 27 Aug 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article