Evidence map›Paper›PMID 38163138›Full record

ArticleHeliyon2024

An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients.

Muhammad Zia Ur Rahman, Muhammad Azeem Akbar, Víctor Leiva, Carlos Martin-Barreiro, Muhammad Imran, Muhammad Tanveer Riaz, Cecilia Castro

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

3 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
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

7 authors at 5 institutions in 6 countries.

Muhammad Zia Ur RahmanDepartment of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan.
Muhammad Azeem AkbarDepartment of Software Engineering, LUT University, Lappeenranta, Finland.
Víctor LeivaEscuela de Ingeniería Industrial, Universidad Católica de Valparaíso, Valparaíso, Chile.
Carlos Martin-BarreiroFacultad de Ciencias Naturales y Matemáticas, ESPOL, Guayaquil, Ecuador.
Muhammad ImranDepartment of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan.
Muhammad Tanveer RiazDepartment of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad, Pakistan.
Cecilia CastroCentre of Mathematics, Universidade do Minho, Braga, Portugal.
University of Engineering and Technology Lahore · PKEscuela Superior Politecnica del Litoral · ECLappeenranta-Lahti University of Technology · FIPontificia Universidad Católica de Valparaíso · CLUniversity of Minho · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.

Indexed as

Arduino microcontrollerFuzzy logicInternet of thingsMobile applicationPersonalized medicineSARS-Cov2Telemetry

Identifiers

PMID38163138
PMCPMC10756970
OpenAlexW4388817509

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.