{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001266197,0.00007597997,0.0001050938,0.0001074559,0.0001447606,0.00003608494,0.00005943374,0.00002956627,0.00001883011],"category_scores_gemma":[0.0001673167,0.00006935988,0.00003656316,0.0004367137,0.0001334228,0.0001403628,0.00008894687,0.0001134194,0.000002286565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001766777,"about_ca_system_score_gemma":0.0001152215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001265201,"about_ca_topic_score_gemma":3.931833e-7,"domain_scores_codex":[0.9989269,0.0000389599,0.0001575943,0.0002892606,0.0004306629,0.0001566303],"domain_scores_gemma":[0.9993209,0.00008594945,0.00004304053,0.00006474732,0.0003618528,0.0001234754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006100551,0.000847867,0.2052582,0.0004420511,0.00003927467,0.0004644629,0.002948454,0.1873483,0.4675351,0.001996407,0.0002498968,0.1322599],"study_design_scores_gemma":[0.0001931635,0.0005038543,0.52047,0.0001411623,0.00001724511,0.0002611428,0.0003528401,0.3677484,0.1096818,0.0004171586,0.0001363261,0.00007687596],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8875521,0.0001526329,0.1113691,0.0004063851,0.0002437202,0.0001637239,0.00001163742,0.0000113637,0.00008933203],"genre_scores_gemma":[0.9926458,0.00006286304,0.006469802,0.0006438742,0.00006078221,0.000002467011,0.000007972483,0.000004746024,0.0001016915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3578533,"threshold_uncertainty_score":0.2828414,"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."}}