DOI: https://doi.org/10.31071/kit2021.17.04

Inventory reference

ISSN 1812-7231(print), ISSN 2786-5800 (Online) Klin. inform. telemed. Volume 16, Issue 17, 2021, Pages 13-19

Author(s)

A. I. Petrenko

Institution(s)

Institute of Applied Systems Analysis of NTUU «Igor Sikorsky KPI», Kyiv

Article title

Medical diagnosis of health at home as a service

Abstract (resume)

Introduction. Remote monitoring of patients is one of the key international areas in the informatization of health care. This is due to the increase in the share of the elderly, the growth of chronic diseases, the overload of the outpatient clinic, the dissatisfaction of patients with their care.

Methods. Information and analytical tools for improving the efficiency of wireless sensor networks (BSM), which use mobile devices and specialized software applications to collect aggregate data on patient health and provide this information to practitioners, researchers and patients themselves, allowing remote diagnosis of various disease, maintaining communication and receiving a preliminary diagnosis and treatment recommendations.

Results. The proposed service-oriented architecture of BSM for medicine with a depository of services created by joint efforts, which in principle allows to solve the problem of compatibility (Interoperability) BSM of different developers in their integration into a global network that may become the largest network of mankind. The possibility of compensating for certain shortcomings and inconveniences of using simple portable body sensors through deep learning methods, in particular, the use of the latest convolutional neural networks (CNN) to establish the necessary diagnostic procedures.

Conclusions. For patients, such systems allow home measurements of the disease, and the doctor, relatives (and / or ambulance) are notified automatically if the patient’s vital signs approach the dangerous level. It is possible for the doctor to remotely monitor the patient’s condition, promptly change his treatment plan, maintain contact with the patient, as well as the opportunity to consult with colleagues and specialists in televised sessions with confidential transmission of patient data.

Keywords

Remote monitoring; Respiratory diseases; Deep learning; Polysomnography; Sleep apnea; Service-oriented architecture; Cloud and edge computing

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