{"id":"W4400764140","doi":"10.1101/2024.07.18.24310625","title":"Autoencoder Imputation of Missing Heterogeneous Data for Alzheimer's Disease Classification","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"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; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Imputation (statistics); Autoencoder; Missing data; Computer science; Artificial intelligence; Machine learning; Artificial neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001507167,0.0002975449,0.0004792263,0.0002050207,0.0004061969,0.00002796383,0.0009142388,0.0005222387,0.0001216168],"category_scores_gemma":[0.001314261,0.0002845788,0.0001494895,0.0001610022,0.000109894,0.00007889867,0.00156076,0.0011181,0.0001882214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001767021,"about_ca_system_score_gemma":0.002046863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005198444,"about_ca_topic_score_gemma":0.0003475519,"domain_scores_codex":[0.9960232,0.0005606746,0.001492303,0.001050701,0.0003812303,0.0004918852],"domain_scores_gemma":[0.9951636,0.001046823,0.0008043774,0.002106863,0.0005955914,0.0002827748],"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":[0.001825319,0.0008290405,0.2673988,0.08144405,0.001867349,0.0001762415,0.02908196,0.02409873,0.002546106,0.01857759,0.04575179,0.526403],"study_design_scores_gemma":[0.00006320018,0.00003084758,0.004957577,0.00206014,0.000511715,7.975599e-7,0.0005669617,0.891372,0.0002975842,0.09543904,0.004394435,0.0003056767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7477412,0.02811132,0.1319865,0.03611041,0.02462925,0.01673641,0.01250309,0.001266586,0.0009152276],"genre_scores_gemma":[0.9889887,0.0001236227,0.005891846,0.0003049291,0.001061683,0.0007660657,0.002625306,0.0001208794,0.0001169602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8672733,"threshold_uncertainty_score":0.9999607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4289580056806098,"score_gpt":0.5364994898318968,"score_spread":0.107541484151287,"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."}}