{"id":"W4402680093","doi":"10.3390/bioengineering11090943","title":"Machine Learning-Driven Prediction of Brain Age for Alzheimer’s Risk: APOE4 Genotype and Gender Effects","year":2024,"lang":"en","type":"article","venue":"Bioengineering","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; University of California, Irvine; University of California, San Francisco; National Institutes of Health; University of Pittsburgh; University of Washington; Johns Hopkins University; Wake Forest University; University of Kansas; Stanford University; New York University; University of Kentucky; University of New Mexico; Cure Alzheimer's Fund; Boston University; York University; Northwestern University; University of California, Davis; University of Wisconsin-Madison; Cleveland Clinic; Emory University; Rush University; Oregon Health and Science University; University of Pennsylvania; University of California, San Diego; Yale University; Vanderbilt University; Columbia University; Mayo Clinic; University of Southern California","keywords":"Apolipoprotein E; Dementia; Random forest; Genotype; Disease; Psychology; Neuroimaging; Medicine; Internal medicine; Neuroscience; Computer science; Machine learning; Biology; Gene","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":[0.005070204,0.0007436203,0.0004554457,0.0006438998,0.0001637939,0.000503594,0.0005220643,0.0004824819,0.0006439181],"category_scores_gemma":[0.007775616,0.0001553004,0.0008124932,0.0003747302,0.0002524978,0.0004875926,0.0003095008,0.0005288546,0.000225734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004201126,"about_ca_system_score_gemma":0.0005906859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004198349,"about_ca_topic_score_gemma":0.003644007,"domain_scores_codex":[0.9993704,0.0003455752,0.00003390342,0.0001411847,0.00005697993,0.00005198051],"domain_scores_gemma":[0.9949522,0.003811945,0.0005147784,0.0002407533,0.0003636183,0.0001166993],"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":[0.00122951,0.0003173034,0.7776186,0.00007788519,0.000622025,0.0001936614,0.0001315138,0.1449834,0.003665471,0.0005948098,0.0007556988,0.06981011],"study_design_scores_gemma":[0.00003044079,0.0005328987,0.1751938,0.00004185861,0.0002728113,0.0002630626,0.00007402098,0.8154153,0.004485793,0.002919744,0.0007273852,0.00004302168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9662703,0.0006978376,0.03179479,0.0002175954,0.00004327895,0.00002668363,0.0004528292,0.000107966,0.0003887286],"genre_scores_gemma":[0.9926972,0.00009744346,0.006654963,0.00003511801,0.0000209321,0.00001479832,0.0002911869,0.00001242757,0.000175941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005070204,"threshold_uncertainty_score":0.02681416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0292965110864957,"score_gpt":0.2996954102175166,"score_spread":0.2703988991310209,"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."}}