{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002426681,0.00009374865,0.0001263569,0.0001130399,0.0001970939,0.00005872307,0.00004739987,0.00002549393,0.000004948744],"category_scores_gemma":[0.0003694402,0.00008773617,0.0000197683,0.0002770659,0.0005314224,0.0001778352,0.00004451939,0.00009073345,3.032675e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008649166,"about_ca_system_score_gemma":0.00004331838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000078592,"about_ca_topic_score_gemma":6.499004e-7,"domain_scores_codex":[0.9989535,0.00005978259,0.0001955755,0.0003688726,0.0002854155,0.0001368551],"domain_scores_gemma":[0.9991756,0.0002244312,0.00009842528,0.0000351185,0.000303974,0.0001624331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007487091,0.0004956566,0.6869869,0.0004044957,0.00002564398,0.00001233758,0.001410169,0.008423042,0.2204967,0.001020632,0.000005637636,0.07997002],"study_design_scores_gemma":[0.0002752398,0.0007308384,0.3835174,0.00005632144,0.00002256687,0.000008542368,0.0003041269,0.6089411,0.0059405,0.0001467103,0.000003883115,0.00005279915],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6706341,0.0001265456,0.3286414,0.0001981173,0.0000184967,0.0003418488,0.000009116131,0.00001063423,0.00001978985],"genre_scores_gemma":[0.9986692,0.00008041193,0.0009023743,0.0002893106,0.000008876404,0.00001052609,0.00002537218,0.000006936415,0.000007000787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.600518,"threshold_uncertainty_score":0.3577777,"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."}}