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Record W3145812136 · doi:10.3390/brainsci11040453

Diagnostic Classification and Biomarker Identification of Alzheimer’s Disease with Random Forest Algorithm

2021· article· en· W3145812136 on OpenAlexfundno aff
Minseok Song, Hyeyoom Jung, Seungyong Lee, Donghyeon Kim, Minkyu Ahn

Bibliographic record

VenueBrain Sciences · 2021
Typearticle
Languageen
FieldNeuroscience
TopicBrain Tumor Detection and Classification
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthServierHandong Global UniversityEisaiNational Research Foundation of KoreaMinistry of Science and ICT, South KoreaBioClinicaNorthern California Institute for Research and EducationF. Hoffmann-La RocheUniversity of Southern CaliforniaBiogenNational Research FoundationEli Lilly and CompanyBristol-Myers SquibbNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of HealthU.S. Department of Defense
KeywordsRandom forestBiomarkerIdentification (biology)DiseaseAlgorithmAlzheimer's diseaseComputer scienceArtificial intelligenceMedicinePathologyBiologyEcology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.004
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.063
GPT teacher head0.305
Teacher spread0.242 · 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
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

Citations109
Published2021
Admission routes1
Has abstractyes

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