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Record W2967732370 · doi:10.18280/ria.330108

Avoiding Interoperability and Delay in Healthcare Monitoring System Using Block Chain Technology

2019· article· en· W2967732370 on OpenAlexvenueno aff
V. Lakshman Narayana, Arepalli Peda Gopi, Kosaraju Chaitanya

Bibliographic record

VenueRevue d intelligence artificielle · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsInteroperabilityBlock (permutation group theory)Health careComputer scienceProcess managementEmbedded systemBusinessOperating systemMathematicsPolitical science

Abstract

fetched live from OpenAlex

Blockchain is using in every aspect now because of its distributed ledger which is immutable. It provides the information to the users directly without any third party involvement. It mediates the transactions directly between the interacting parties securely. It also eliminates the friction and also the cost of current intermediaries. It is now using in healthcare system to provide the interoperability, security, decentralization and other. EMR is presently using in healthcare which has some issues. The issues in healthcare are patient cannot access the data of his/her own health information. So by this healthcare has issues like interoperability and delay in communication and some other. These issues can be solved by using the Blockchain in healthcare. By this Blockchain provide security by giving the patients to access their own data rather than provider.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.296
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2019
Admission routes1
Has abstractyes

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