{"id":"W4306722543","doi":"10.1155/2022/1419310","title":"A Deep Longitudinal Model for Mild Cognitive Impairment to Alzheimer’s Disease Conversion Prediction in Low-Income Countries","year":2022,"lang":"en","type":"article","venue":"Applied Computational Intelligence and Soft Computing","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Alzheimer Immunotherapy Research And Development; Johnson and Johnson Pharmaceutical Research and Development; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Neuropsychology; Neuroimaging; Alzheimer's disease; Regression; Medicine; Alzheimer's Disease Neuroimaging Initiative; Deep learning; Biomarker; Cognitive impairment; Artificial neural network; Disease; Artificial intelligence; Cognitive decline; Cognition; Dementia; Psychology; Internal medicine; Computer science; 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":[0.0004899293,0.0001771282,0.0002299255,0.0002838878,0.0004667643,0.00004941606,0.00009056785,0.00002774827,0.00007297196],"category_scores_gemma":[0.00002874199,0.0001914447,0.00006716382,0.0002910223,0.00008523864,0.00006598739,0.0002548594,0.000206874,0.00001613903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443959,"about_ca_system_score_gemma":0.0002146445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001258036,"about_ca_topic_score_gemma":0.000002767861,"domain_scores_codex":[0.9981536,0.00003612003,0.000377075,0.0004886758,0.0005799677,0.0003645368],"domain_scores_gemma":[0.9989197,0.0004516381,0.0000765498,0.00007186834,0.0002397584,0.0002404482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003062946,0.0004286106,0.164148,0.0001991938,0.0001205692,0.0000279867,0.001789874,0.8188881,0.000004604399,0.002923817,0.00007523564,0.008331113],"study_design_scores_gemma":[0.001010073,0.0004273844,0.1645175,0.0001342743,0.00008508407,0.00001071322,0.001082867,0.8293457,0.00007376273,0.00315108,0.00001251969,0.000149019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4344574,0.0001368128,0.5634593,0.0003562011,0.00004736805,0.00138884,0.00004153483,0.00003657434,0.00007598107],"genre_scores_gemma":[0.9954212,0.000007494211,0.002735414,0.001133088,0.00005881837,0.0003465714,0.0002422313,0.00001805986,0.00003713539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5609638,"threshold_uncertainty_score":0.7806886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.036013368653617,"score_gpt":0.3279011572015639,"score_spread":0.2918877885479469,"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."}}