Evidence map›Paper›PMID 38020047›Full record

ArticleInternational journal of telemedicine and applications2023

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Htet Yamin Ko Ko, Nitin Kumar Tripathi, Chitrini Mozumder, Sombat Muengtaweepongsa, Indrajit Pal

Open access · goldAbstract read
In one paragraph

Article in International journal of telemedicine and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Htet Yamin Ko KoDepartment of Information and Communication Technologies, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand.ORCID https://orcid.org/0000-0002-8242-0767
Nitin Kumar TripathiDepartment of Information and Communication Technologies, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand.
Chitrini MozumderDepartment of Information and Communication Technologies, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand.
Sombat MuengtaweepongsaCenter of Excellence in Stroke, Faculty of Medicine, Thammasat University, Pathum Thani 10121, Thailand.
Indrajit PalSchool of Environment, Resources and Development, Asian Institute of Technology, Pathum Thani 12120, Thailand.
Asian Institute of Technology · THThammasat University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

Identifiers

PMID38020047
PMCPMC10681793
OpenAlexW4388821716

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.