Evidence map›Paper›PMID 37790086›Full record

ArticleFrontiers in big data2023

An intelligent telemonitoring application for coronavirus patients: reCOVeryaID.

Daniela D'Auria, Raffaele Russo, Alfonso Fedele, Federica Addabbo, Diego Calvanese

Abstract read
In one paragraph

Article in Frontiers in big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Daniela D'AuriaFaculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
Raffaele RussoPineta Grande Hospital, Caserta, Italy.
Alfonso FedeleUniversity Riuniti Hospital, Ancona, Italy.
Federica AddabboKronosan Srl, Montevergine Hospital, Mercogliano, Italy.
Diego CalvaneseFaculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.

Indexed as

artificial intelligencecoronavirusCOVID-19eHealthlong-term monitoringrule-based systemtelehealthtelemedicine

Identifiers

PMID37790086
PMCPMC10543687

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.