Evidence map›Paper›PMID 35071732›Full record

ArticleInformatics in medicine unlocked2022

COVID and nutrition: A machine learning perspective.

Nafiseh Jafari, Mohammad Reza Besharati, Mohammad Izadi, Alireza Talebpour

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Informatics in medicine unlocked, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 42% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 3 institutions in 1 country.

Nafiseh JafariEngineering Department, University of Qom, Qom, Iran.
Mohammad Reza BesharatiDepartment of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Mohammad IzadiDepartment of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Alireza TalebpourComputer Science and Engineering Department, Shahid Beheshti University, Tehran, Iran.
Sharif University of Technology · IRShahid Beheshti University · IRUniversity of Qom · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A self-report questionnaire survey was conducted online to collect big data from over 16000 Iranian families (who were the residents of 1000 urban and rural areas of Iran). The resulting data storage contained over 1 M records of data and over 1G records of automatically inferred information. Based on this data storage, a series of machine learning experiments was conducted to investigate the relationship between nutrition and the risk of contracting COVID-19. With highly accurate scores, the findings strongly suggest that foods and water sources containing certain natural bioactive and phytochemical agents may help to reduce the risk of apparent COVID-19 infection.

Indexed as

Big dataCOVID-19DietMachine learningMultilayer perceptronNutritionRandom forest

Identifiers

PMID35071732
PMCPMC8767975
OpenAlexW4206336130

What OpenQuestion holds

Textmetadata
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.