{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005226407,0.0006189072,0.0003139883,0.001196253,0.0001394502,0.0004157261,0.0001889309,0.0003190247,0.0004244318],"category_scores_gemma":[0.001875582,0.00008933945,0.0002845993,0.0003868096,0.0001433406,0.0003717762,0.0002392277,0.0003442109,0.0001168939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001979506,"about_ca_system_score_gemma":0.0002485714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002933509,"about_ca_topic_score_gemma":0.006634728,"domain_scores_codex":[0.9998909,0.00002443314,0.00001123813,0.00003550179,0.00002038294,0.00001748497],"domain_scores_gemma":[0.9995887,0.0001492306,0.0001284339,0.00002535245,0.00006918779,0.00003903232],"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":[0.0007171027,0.0003411165,0.7159554,0.0001586702,0.0003648629,0.0006516808,0.0001375174,0.04022623,0.02684294,0.0008679032,0.001419137,0.2123174],"study_design_scores_gemma":[0.0000205571,0.000283215,0.6145896,0.00004433329,0.0001827857,0.0006578054,0.0001296128,0.3729764,0.007235608,0.003145311,0.0006963252,0.00003835289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966126,0.0006495154,0.03152995,0.0001219335,0.00001869332,0.00003731801,0.0004657721,0.000138142,0.0009127623],"genre_scores_gemma":[0.9926761,0.0001472205,0.006656149,0.000009438422,0.0000131567,0.00001371206,0.0003055683,0.00000369792,0.0001750381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002933509,"threshold_uncertainty_score":0.005832851,"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."}}