{"id":"W2336579138","doi":"10.3389/fnagi.2016.00076","title":"Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features","year":2016,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; Fundamental Research Funds for the Central Universities; National Institutes of Health; H. Lundbeck A/S; Servier; National Natural Science Foundation of China; Eisai; Genentech; IXICO; Ministerio de Ciencia e Innovación; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; University of California, San Diego; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Discriminative model; Support vector machine; Feature selection; Magnetic resonance imaging; Pattern recognition (psychology); Artificial intelligence; Cognitive impairment; Stability (learning theory); Feature (linguistics); Computer science; Machine learning; Disease; Medicine; Internal medicine; 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":[0.0001578468,0.0001638822,0.0001872904,0.0001630755,0.0002681473,0.00003491604,0.0001873032,0.00002794929,0.000003131684],"category_scores_gemma":[0.001540254,0.0001281074,0.00003637209,0.000476051,0.0004885482,0.0003961396,0.0002526647,0.0000969499,6.943074e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007211235,"about_ca_system_score_gemma":0.00004765225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005307157,"about_ca_topic_score_gemma":0.000002903649,"domain_scores_codex":[0.9981825,0.000152452,0.0001818155,0.0007615755,0.0003887755,0.0003328568],"domain_scores_gemma":[0.9986907,0.0008544719,0.000101896,0.0001644643,0.00003537435,0.0001530703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000493762,0.00004008537,0.8966363,0.00001534084,0.000007744236,0.00003079007,0.0005074602,0.002575089,0.0932151,0.00005545937,0.004893378,0.001529482],"study_design_scores_gemma":[0.00070937,0.0001330241,0.9528128,0.0002771881,0.0000347616,0.000005256008,0.0001103567,0.01165171,0.03290316,0.001018888,0.0001620944,0.0001813825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834028,0.0003485696,0.0105385,0.001646929,0.003356467,0.0004161003,0.0002269522,0.0000471704,0.00001651847],"genre_scores_gemma":[0.9966708,0.00005978199,0.001223212,0.001906851,0.00009137759,0.000009929071,7.307054e-7,0.00001130543,0.00002604933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06031194,"threshold_uncertainty_score":0.5224066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03366418423644967,"score_gpt":0.2605426633523935,"score_spread":0.2268784791159438,"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."}}