{"id":"W3202517842","doi":"10.1155/2021/6628036","title":"Early MCI‐to‐AD Conversion Prediction Using Future Value Forecasting of Multimodal Features","year":2021,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Canadian Institutes of Health Research; University of Southern California; National Institute of Biomedical Imaging and Bioengineering; Northern California Institute for Research and Education; Foundation for the National Institutes of Health; U.S. Department of Defense","keywords":"Neuroimaging; Cohort; Support vector machine; Neuropsychology; Artificial intelligence; Multivariate statistics; Alzheimer's Disease Neuroimaging Initiative; Disease; Cognitive impairment; Computer science; Dementia; Population; Cognition; Medicine; Machine learning; Psychology; Internal medicine; Psychiatry","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":[0.001165201,0.0006591816,0.0005634677,0.0008178611,0.0001817304,0.0005888863,0.0004235228,0.0004099979,0.0005954329],"category_scores_gemma":[0.003717556,0.0001570426,0.0004382716,0.0004573429,0.0001628747,0.0005455405,0.000387249,0.0006891602,0.0002393247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002694504,"about_ca_system_score_gemma":0.0004759291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004230402,"about_ca_topic_score_gemma":0.004751467,"domain_scores_codex":[0.9997939,0.00006203445,0.00001770562,0.00005709157,0.00003748676,0.00003165677],"domain_scores_gemma":[0.999076,0.0005289296,0.0001499892,0.00007919962,0.0001231136,0.00004277248],"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":[0.0008048989,0.0004611664,0.1211064,0.00008816006,0.000187728,0.0003371675,0.0001812398,0.4150215,0.009822443,0.00138079,0.001847223,0.4487613],"study_design_scores_gemma":[0.000004815931,0.00008859201,0.01194497,0.00001403535,0.00001961067,0.00005128158,0.00002008829,0.9845582,0.001863379,0.00116439,0.0002556761,0.00001496702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6934023,0.001028819,0.3021608,0.0004751871,0.00007230472,0.00006639796,0.000619754,0.0007893899,0.001385145],"genre_scores_gemma":[0.9762322,0.0001585889,0.02276796,0.00002649242,0.0000264521,0.00002342005,0.0002982462,0.00001282856,0.0004538206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004230402,"threshold_uncertainty_score":0.008411586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06490158264836303,"score_gpt":0.3459365483166308,"score_spread":0.2810349656682678,"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."}}