{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001267272,0.000131601,0.0001413884,0.00005893304,0.0002271027,0.00002913027,0.00007553677,0.00003211807,0.000005488376],"category_scores_gemma":[0.0001687145,0.000127045,0.00008492967,0.0001687117,0.00002526353,0.00005694151,0.00001094555,0.0002714252,0.000002526882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008395581,"about_ca_system_score_gemma":0.0002301943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.232148e-7,"about_ca_topic_score_gemma":4.842272e-7,"domain_scores_codex":[0.998903,0.00004401579,0.0001507471,0.0004276339,0.0001908349,0.0002837281],"domain_scores_gemma":[0.9990522,0.0000538681,0.00006841651,0.0004625582,0.0002221305,0.0001408632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009506871,0.002810985,0.02124425,0.0001303157,0.00004618842,0.00002549856,0.00008932173,0.006477824,0.529696,0.0257069,0.003066801,0.4097552],"study_design_scores_gemma":[0.0009600623,0.0002584664,0.009561891,0.00004451031,0.0002376397,0.00001617376,0.00005203944,0.81542,0.02549312,0.001832607,0.1458848,0.0002387553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02159315,0.002898842,0.9649889,0.00740919,0.00002763422,0.0009818273,0.000009895005,0.0006347456,0.001455789],"genre_scores_gemma":[0.9528686,0.0002116933,0.04431969,0.001507301,0.00009693096,0.0002883165,0.0004619965,0.00005563731,0.0001898244],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9312754,"threshold_uncertainty_score":0.5180744,"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."}}