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Enhancing Security of RFID Implants in the Healthcare Industry

2024· article· en· W4409660644 on OpenAlexaff
Bewaji Oluwaseun, Abiodun Ogunmilua, Eslam G. AbdAllah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsHealth careHealthcare industryComputer securityBusinessComputer scienceEconomics

Abstract

fetched live from OpenAlex

With the help of Radio Frequency Identification (RFID) technology, data may be efficiently, automatically, and instantly collected and sent without the need for human participation. This technology has been regarded as a potential solution to lessen issues that jeopardize patient safety or enhance its management. This paper proposes an architecture for the use of RFID in emergency medical services (EMS). The goal of RFID in the proposed architecture is to address problems such as the inadequate medical diagnosis of patients in emergency situations, inadequate identification of incapacitated patients, and inadequate medication administration to patients. All these problems are caused by a lack of patient information, such as their medical conditions and histories or whether they are allergic to certain medications. The security and privacy flaws in the suggested architecture are also covered in this paper, along with recommendations for how to fix them.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.270
Teacher spread0.261 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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