Scalable real-time health data sensing and analysis enabling collaborative care delivery

被引:1
作者
Dimitriadis, Ilias [1 ]
Mavroudopoulos, Ioannis [1 ]
Kyrama, Styliani [1 ]
Toliopoulos, Theodoros [1 ]
Gounaris, Anastasios [1 ]
Vakali, Athena [1 ]
Billis, Antonis [2 ]
Bamidis, Panagiotis [2 ]
机构
[1] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki, Greece
[2] Aristotle Univ Thessaloniki, Sch Med, Thessaloniki, Greece
关键词
Data ingestion; Streaming analytics; Frailty monitoring; Cloud processing; Edge processing; OLDER PATIENTS; TREND ANALYSIS; SMART HOME; HEART-RATE; CANCER; ALGORITHMS; EFFICIENT; FRAILTY;
D O I
10.1007/s13278-022-00891-y
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This work describes a novel end-to-end data ingestion and runtime processing pipeline, which is a core part of a technical solution aiming to monitor frailty indices of patients during and after treatment and improve their quality of life. The focus of this work is on the technical architectural details and the functionalities provided, which have been developed in a manner that are extensible, scalable and fault-tolerant by design. Extensibility refers to both data sources and the exact specification of analysis techniques. Our platform can combine data not only from multiple sensor types but also from electronic health records. Also, the analysis component can process the patient data both individually and in combination with other patients, while exploiting both cloud and edge resources. We have shown concrete examples of advanced analytics and evaluated the scalability of the system, which has been fully prototyped.
引用
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页数:22
相关论文
共 76 条
  • [51] Pereira J, 2019, LAT-AM SYMP DEP COMP, P1
  • [52] RETRACTED: Resource Management and Task Scheduling for IoT using Mobile Edge Computing (Retracted Article)
    Quasim, Mohammad Tabrez
    [J]. WIRELESS PERSONAL COMMUNICATIONS, 2022, 127 (SUPPL 1) : 35 - 35
  • [53] Event-driven IoT architecture for data analysis of reliable healthcare application using complex event processing
    Rahmani, Amir Masoud
    Babaei, Zahra
    Souri, Alireza
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2021, 24 (02): : 1347 - 1360
  • [54] A Framework for Service Robots in Smart Home: An Efficient Solution for Domestic Healthcare
    Ramoly, N.
    Bouzeghoub, A.
    Finance, B.
    [J]. IRBM, 2018, 39 (06) : 413 - 420
  • [55] RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices
    Ranjan, Yatharth
    Rashid, Zulqarnain
    Stewart, Callum
    Conde, Pauline
    Begale, Mark
    Verbeeck, Denny
    Boettcher, Sebastian
    Dobson, Richard
    Folarin, Amos
    [J]. JMIR MHEALTH AND UHEALTH, 2019, 7 (08):
  • [56] A PSYCHOMETRIC INVESTIGATION OF THE STANDARD AND SHORT FORM BECK DEPRESSION INVENTORY
    REYNOLDS, WM
    GOULD, JW
    [J]. JOURNAL OF CONSULTING AND CLINICAL PSYCHOLOGY, 1981, 49 (02) : 306 - 307
  • [57] Riboni D, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATION WORKSHOPS (PERCOM WORKSHOPS)
  • [58] Analysis of the Security and Privacy Requirements of Cloud-Based Electronic Health Records Systems
    Rodrigues, Joel J. P. C.
    de la Torre, Isabel
    Fernandez, Gonzalo
    Lopez-Coronado, Miguel
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2013, 15 (08)
  • [59] Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks
    Roldan, Jose
    Boubeta-Puig, Juan
    Luis Martinez, Jose
    Ortiz, Guadalupe
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2020, 149
  • [60] Healthcare and anomaly detection: using machine learning to predict anomalies in heart rate data
    Sabic, Edin
    Keeley, David
    Henderson, Bailey
    Nannemann, Sara
    [J]. AI & SOCIETY, 2021, 36 (01) : 149 - 158