{"id":"W4403570710","doi":"10.48550/arxiv.2410.10374","title":"Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Università Campus Bio-Medico di Roma; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Class (philosophy); Diversity (politics); Computer science; Artificial intelligence; Disease; Machine learning; Medicine; Sociology; Internal medicine; Anthropology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003672993,0.00136854,0.001495796,0.001557747,0.0006876467,0.0008770491,0.001081836,0.001073346,0.0010504],"category_scores_gemma":[0.004650355,0.0002618591,0.001138655,0.0008756283,0.0003111031,0.001142217,0.001406862,0.001492621,0.0005707279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005908467,"about_ca_system_score_gemma":0.0007750368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003629599,"about_ca_topic_score_gemma":0.003774061,"domain_scores_codex":[0.998821,0.0004661836,0.0000464129,0.000278273,0.0002588504,0.0001292321],"domain_scores_gemma":[0.9984383,0.0006642502,0.0001167935,0.0002236078,0.0004244493,0.0001326842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009201985,0.0005810752,0.02343746,0.0001158058,0.0007644255,0.0001768421,0.0002109118,0.279073,0.01013036,0.001636931,0.01122046,0.6717326],"study_design_scores_gemma":[0.0000198036,0.0001801551,0.003274939,0.00002181165,0.00009900166,0.00005896946,0.00005126337,0.9884574,0.002650003,0.003689521,0.001475353,0.00002181581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2349309,0.00629423,0.748431,0.001488017,0.0004890735,0.0001852241,0.001119171,0.002772122,0.004290326],"genre_scores_gemma":[0.9173116,0.0008744003,0.07619723,0.0004814486,0.0004474189,0.0001377434,0.002195509,0.00009759181,0.002257053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003672993,"threshold_uncertainty_score":0.01942486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2458194905120243,"score_gpt":0.3369340499919445,"score_spread":0.09111455947992017,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}