{"id":"W3034075224","doi":"10.1155/2020/8015156","title":"An Efficient Combination among sMRI, CSF, Cognitive Score, and <i>APOE ε</i>4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine","year":2020,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; GE Healthcare; Genentech; National Institutes of Health; Takeda Pharmaceutical Company; IXICO; H. Lundbeck A/S; Servier; Eisai; Meso Scale Diagnostics; National Research Foundation of Korea; Elan; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; Biogen; BioClinica; Roche; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Alzheimer's Drug Discovery Foundation; Johnson and Johnson Pharmaceutical Research and Development; National Research Foundation; AbbVie; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Dementia; Feature selection; Cognition; Atrophy; Magnetic resonance imaging; Medicine; Disease; Artificial intelligence; Computer science; Psychology; Neuroscience; Pathology; Radiology","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":[0.002208806,0.001123925,0.001399018,0.001572877,0.0003686158,0.00073059,0.0006589458,0.0008327524,0.0006201288],"category_scores_gemma":[0.002871349,0.000310179,0.001263063,0.001111922,0.0002984199,0.0006142234,0.0007015845,0.0007177719,0.0006310599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261342,"about_ca_system_score_gemma":0.0006400473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00105278,"about_ca_topic_score_gemma":0.001346199,"domain_scores_codex":[0.9992656,0.0002058351,0.0000719278,0.0002227645,0.000135603,0.00009828598],"domain_scores_gemma":[0.9992355,0.0003207445,0.00009484455,0.00006177382,0.0002392033,0.00004799722],"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":[0.001396339,0.0007850943,0.04922474,0.0002722082,0.0006625291,0.0006707232,0.0002600218,0.07605625,0.09158375,0.0007539611,0.004067666,0.7742668],"study_design_scores_gemma":[0.00004272819,0.0004609956,0.0296837,0.00004221815,0.0002485287,0.0005486908,0.00008200992,0.9512413,0.01505384,0.001531001,0.001004072,0.00006102894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3013345,0.001016243,0.6945259,0.000228778,0.00008834693,0.0001226013,0.0003483053,0.001663782,0.0006715336],"genre_scores_gemma":[0.7635354,0.0002345947,0.2336642,0.0001050914,0.00006988708,0.0001997467,0.001129669,0.00007532693,0.0009860904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002208806,"threshold_uncertainty_score":0.01168138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1466268577108869,"score_gpt":0.3722594762690649,"score_spread":0.225632618558178,"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."}}