{"id":"W3122665304","doi":"10.3390/electronics10030249","title":"An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Ministero dello Sviluppo Economico; Biogen; BioClinica; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; U.S. Department of Defense; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Computer science; Machine learning; Concatenation (mathematics); Diffusion MRI; Feature selection; Ensemble learning; Exploit; Neuroimaging; Curse of dimensionality; Pattern recognition (psychology); Magnetic resonance imaging; Mathematics","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":[0.00201312,0.0007223031,0.001131371,0.001520084,0.0004987906,0.0007823965,0.0007827254,0.0006922385,0.0008219935],"category_scores_gemma":[0.002779835,0.0001759147,0.000948686,0.0009946224,0.0002517476,0.0008903582,0.0007248817,0.0009178052,0.0003439537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002763012,"about_ca_system_score_gemma":0.0005087143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002066035,"about_ca_topic_score_gemma":0.002655274,"domain_scores_codex":[0.9992936,0.000200096,0.00006573769,0.0001621518,0.0001968409,0.0000816278],"domain_scores_gemma":[0.9990315,0.0003359922,0.00007246265,0.0001488405,0.000353029,0.00005823254],"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":[0.0002354056,0.0001985034,0.007542688,0.00006245491,0.0003218227,0.0001076527,0.0001172756,0.1546838,0.01247298,0.005282683,0.003203258,0.8157715],"study_design_scores_gemma":[0.000005056289,0.00008326087,0.001306826,0.000009580448,0.00005946018,0.00005038102,0.0000184382,0.9918489,0.002391648,0.003297955,0.0009162527,0.00001214737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05950258,0.001057724,0.9373809,0.0002381583,0.0001331425,0.00005221032,0.0001343794,0.0004814032,0.001019487],"genre_scores_gemma":[0.7477927,0.0006957077,0.2485099,0.0001227761,0.0002210333,0.0001154432,0.0006140445,0.00006843321,0.001859905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002066035,"threshold_uncertainty_score":0.01064652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07979652277542423,"score_gpt":0.3556719242560934,"score_spread":0.2758754014806691,"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."}}