Evidence map›Paper›PMID 34511692›Full record

ArticleExpert systems2022

Blockchain technology: A DNN token-based approach in healthcare and COVID-19 to generate extracted data.

Basetty Mallikarjuna, Gulshan Shrivastava, Meenakshi Sharma

Open access · bronzeAbstract read
In one paragraph

Article in Expert systems, 2022. 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
8.5field-weighted citation impact, top 2% 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, 37 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

3 authors at 2 institutions in 1 country.

Basetty MallikarjunaSchool of Computing Science and Engineering Galgotias University Greater Noida India.ORCID https://orcid.org/0000-0003-4354-4684
Gulshan ShrivastavaDepartment of Computer Science and Engineering Sharda University Greater Noida India.ORCID https://orcid.org/0000-0003-3671-4921
Meenakshi SharmaSchool of Computing Science and Engineering Galgotias University Greater Noida India.
Galgotias University · INSharda University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The healthcare technologies in COVID-19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19.

Indexed as

BlockchainCOVID‐19 pandemicdeep neural networkelectronic health recordsfeature extracted data

Identifiers

PMID34511692
PMCPMC8420355
OpenAlexW3183160983

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.