{"id":"W2986832571","doi":"10.1101/836510","title":"Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Instituto Tecnológico y de Estudios Superiores de Monterrey; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación; 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; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Proportional hazards model; Multivariate statistics; Neuroimaging; Concordance; Hazard ratio; Regression; Artificial intelligence; Cognitive impairment; Regression analysis; Feature (linguistics); Computer science; Machine learning; Disease; Medicine; Psychology; Internal medicine; Statistics; Neuroscience; Confidence interval; Mathematics","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":[0.01847996,0.001157337,0.001200894,0.00299157,0.0004940731,0.001497756,0.001315992,0.001167155,0.001139944],"category_scores_gemma":[0.03349816,0.0005064778,0.002086869,0.001085168,0.000782,0.0008517469,0.001083177,0.002201052,0.0002483899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008909939,"about_ca_system_score_gemma":0.001037091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005761132,"about_ca_topic_score_gemma":0.003248813,"domain_scores_codex":[0.996052,0.003189432,0.0001197903,0.0003130958,0.0001650396,0.0001606598],"domain_scores_gemma":[0.949632,0.04721178,0.001061797,0.001001027,0.0007894646,0.0003039393],"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":[0.0004585432,0.0002416604,0.05328702,0.00006554346,0.0004620501,0.0001214213,0.0001098775,0.9125342,0.0006114991,0.002194348,0.0007246539,0.02918917],"study_design_scores_gemma":[0.000008324849,0.00005364968,0.00191443,0.000007658487,0.00002077893,0.00001743347,0.00001280707,0.9955093,0.0001365147,0.002258303,0.00005252728,0.000008324277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4927801,0.001300427,0.5023591,0.001148753,0.0000499186,0.0001186858,0.0008093696,0.0007404741,0.0006931771],"genre_scores_gemma":[0.9656364,0.0001612264,0.03249185,0.00014066,0.00005281651,0.0001020782,0.0008615282,0.00004416706,0.0005093158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01847996,"threshold_uncertainty_score":0.09773254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06691050260485436,"score_gpt":0.2554467426148046,"score_spread":0.1885362400099502,"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."}}