{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004423163,0.0007985294,0.001137512,0.0006520094,0.0003780806,0.0006396,0.0009268955,0.0008018039,0.0007608866],"category_scores_gemma":[0.009372885,0.000438838,0.001144104,0.0007773823,0.0004762232,0.0008885225,0.0007881449,0.002264617,0.0003901881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005394897,"about_ca_system_score_gemma":0.00101908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004173543,"about_ca_topic_score_gemma":0.004982579,"domain_scores_codex":[0.9990434,0.0003883445,0.00007647331,0.0002466751,0.0001503478,0.00009482215],"domain_scores_gemma":[0.9966068,0.002082553,0.0002867071,0.0004358931,0.0005235393,0.00006443972],"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":[0.0005617372,0.0003045438,0.01728058,0.0002053478,0.0005022816,0.000260465,0.0002287201,0.6812402,0.007138447,0.004759761,0.004377109,0.2831408],"study_design_scores_gemma":[0.000009732241,0.0000456441,0.002207376,0.00002400834,0.00002618584,0.00003579289,0.00001933023,0.991769,0.002269356,0.003079267,0.0005008677,0.00001351202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09081829,0.001042198,0.9053481,0.0004116778,0.00009230775,0.00005352522,0.0005296197,0.0009870538,0.0007171655],"genre_scores_gemma":[0.7602023,0.0007046182,0.2340678,0.0003144708,0.0001007731,0.0001706841,0.002506809,0.0001024261,0.001830083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004423163,"threshold_uncertainty_score":0.02339214,"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."}}