An intelligent healthcare system with data priority based on multi vital biosignals. (March 2020)

• Record Type: Journal Article
• Authors: Xie, Rongjun; Khalil, Ibrahim; Badsha, Shahriar; Atiquzzaman, Mohammed
• Is Part Of: Computer methods and programs in biomedicine. Volume 185(2020)
• Publication Date: 2020-03
• Subjects: Healthcare system -- Data priority -- Vital biosignals; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105126
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The data collected from patients are organized based on a time window, which is grouped into a Time-Window Data Chunk. The proposed method can decide which TWDC needs to be transmitted based on the priority from data assessment. The method is efficient in data collection and optimizes the processing order in prediction. Abstract: Background and Objective: Home-based personal healthcare systems are becoming popular and affordable due to the development of Internet of Things (IoT) devices. However, with an increasing number of users, such healthcare systems are challenged to store and process enormous volumes of data. For instance, multi-biosignal data are collected continuously from patients using IoT device like body sensors and are sent to the server by portable devices for further analysis (e.g., knowledge discovery or the clinical event prediction). These enormous amount of data from large number of patients are causing the transmission overhead and high latency in the network which are responsible for inefficiency issues in clinical event prediction. To address these problems, in this paper, data assessment method is introduced to improve the efficiency in data collection and data prediction. Methods: The assessment algorithm is inspired by National Early Warning Score (NEWS) used in Emergency Department. In our method, only the abnormal time-sequence data for analysis are sent to the server. Thus, the waiting time of data before prediction can be optimized because data with higher priority are processed in front of those with lower priority, which helps our system to provide diagnostic decisions in a proper time according to patients' urgency. Results: Our experiments show that the proposed model ideally can save 20% volume of data in the collection and can reduce 75% waiting time of data with the highest priority before predicting. In addition, the waiting time of data for further analysis is optimized compared to the normal processing flow. Conclusion: The paper introduces an enhanced healthcare system with assessing data priority in order to optimize the data collection and the prediction in terms of data size and waiting time.