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Record W4360841925 · doi:10.4018/ijssci.320499

Knowledge Discovery of Hospital Medical Technology Based on Partial Ordered Structure Diagrams

2023· article· en· W4360841925 on OpenAlex
Dingju Zhu, Jianbin Tan, Guangbo Luo, Haoxiang Gu, Zhanhao Ye, Renfeng Deng, Keyi He, Kai Leung Yung, W.H. Ip

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Software Science and Computational Intelligence · 2023
Typearticle
Languageen
FieldComputer Science
TopicRough Sets and Fuzzy Logic
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsComputer sciencePartially ordered setDiagramContext (archaeology)Set (abstract data type)Data miningAlgorithmTheoretical computer scienceDiscrete mathematicsMathematicsDatabase

Abstract

fetched live from OpenAlex

So far, no research has used the partial order algorithm for the mining of hospital medical technology. This paper proposed a novel knowledge discovery method of hospital medical technology based on partial ordered structure diagrams, constructed attribute partial ordered structure diagram and object partial ordered structure diagram for the formal context constructed by hospital set and medical technology set, and finally analyzed them using the knowledge discovery method. The experiments show that the partial ordered structure diagram can effectively visualize the structural relationships between hospital sets and medical technology sets, and the distribution characteristics of medical technology sets in hospital sets and the rules of medical technology sets owned by hospital sets can be obtained based on the node, branch, and group structure relationships of the partial ordered structure diagram.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.015
GPT teacher head0.302
Teacher spread0.286 · 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