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

来源：

SOCIAL NETWORK ANALYSIS AND MINING | 2022年 / 12卷 / 01期

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

页数：22

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