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Enabling Communication and Networking Technologies for 6G in Healthcare Sector

2023· article· en· W4391020952 on OpenAlexaff
Mereena William, Sana Sharif, Waleed Ejaz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsLakehead University
Fundersnot available
KeywordsHealth careDisseminationThe InternetComputer scienceBusinessComputer securityKnowledge managementTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

Future sixth-generation (6G) networks and the Internet of Things (IoT) are expected to transform the healthcare sector by offering opportunities to enhance patient outcomes and effective resource optimization. This can result in healthcare systems that collect, analyze, and disseminate real-time data to improve patient care and clinical decision-making. This paper surveys enabling communication and networking technologies in the healthcare sector. We provide a discussion on the healthcare monitoring system (HCMS), which is pivotal for functioning any enabling technology in healthcare. We emphasize how the enabling technologies in 6G are important in the healthcare sector and provide a comparison. Solutions for mission-critical healthcare are also discussed. We highlight security and privacy challenges and discuss blockchain technology for secure and efficient data management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.297
Teacher spread0.238 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes1
Has abstractyes

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