{"id":"W2575828404","doi":"10.1038/srep39880","title":"Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Science and Technology Planning Project of Guangdong Province; University of California, San Diego; National Institutes of Health; Eisai; Genentech; National Natural Science Foundation of China; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; F. Hoffmann-La Roche; Synarc; University of Southern California; Elan; Novartis; Medpace; GlaxoSmithKline; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Neuroimaging; Computer science; Classifier (UML); Disease; Cognitive impairment; Construct (python library); Pattern recognition (psychology); Alzheimer's disease; Longitudinal data; Machine learning; Medicine; Data mining; Internal medicine; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003865884,0.0006614843,0.0009561372,0.001973359,0.0006592121,0.0007804802,0.001437375,0.0008445578,0.001159417],"category_scores_gemma":[0.00385402,0.00025909,0.001240209,0.001194853,0.0003171881,0.0006323635,0.0007335442,0.001315906,0.0004243835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226516,"about_ca_system_score_gemma":0.001757031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03485361,"about_ca_topic_score_gemma":0.02981552,"domain_scores_codex":[0.9986132,0.0005464322,0.00009281389,0.0003058365,0.0002589479,0.0001827188],"domain_scores_gemma":[0.9984549,0.0006471081,0.0001935751,0.0001274971,0.0004723062,0.0001045771],"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":[0.0006662047,0.0009707,0.05785008,0.0001571412,0.0006446174,0.0003754604,0.0004700624,0.3556187,0.005955927,0.01007517,0.007563169,0.5596527],"study_design_scores_gemma":[0.000008545542,0.00005109835,0.002977805,0.000007792939,0.00003271005,0.00001891772,0.00001985718,0.9947168,0.0002248761,0.001658548,0.0002702564,0.00001283933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1202318,0.001384278,0.8743877,0.0006109453,0.0001330308,0.0002492512,0.0007309297,0.001103414,0.001168618],"genre_scores_gemma":[0.7774708,0.0005045804,0.2179919,0.0001612483,0.0001855401,0.0003741741,0.001405938,0.00004174976,0.001864137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03485361,"threshold_uncertainty_score":0.06930149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1380043633566299,"score_gpt":0.3734164424876583,"score_spread":0.2354120791310284,"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."}}