{"id":"W3023267018","doi":"10.1101/2020.04.23.20077412","title":"A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"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; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Science Foundation","keywords":"Multimodality; Computer science; Predictive analytics; Transfer of learning; Machine learning; Artificial intelligence; Analytics; Modalities; Maximization; Data mining","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":[0.005126185,0.001231726,0.001690305,0.00122787,0.0005946346,0.001567301,0.003248692,0.002398827,0.003425838],"category_scores_gemma":[0.0126387,0.0006448514,0.001466307,0.001329408,0.001786349,0.002690043,0.002344186,0.003281152,0.0008235035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001749675,"about_ca_system_score_gemma":0.001298461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008657272,"about_ca_topic_score_gemma":0.004122036,"domain_scores_codex":[0.9985441,0.0004541527,0.00008293708,0.0004965044,0.0002459543,0.0001764315],"domain_scores_gemma":[0.9939758,0.004235142,0.0004668697,0.0003527157,0.0008164567,0.0001530502],"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":[0.0001339752,0.00009584706,0.002567255,0.00007817022,0.00007485992,0.0001686462,0.0001251871,0.932422,0.0008401709,0.01709693,0.001445264,0.04495169],"study_design_scores_gemma":[0.000003405102,0.00001048198,0.00009906708,0.000004967124,0.000004878378,0.000009274492,0.000004690577,0.9947913,0.0001209486,0.004824292,0.000122774,0.00000390143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0291137,0.0003881642,0.9674265,0.0009846907,0.00004275604,0.00008448551,0.0002899788,0.0004273507,0.001242334],"genre_scores_gemma":[0.8961469,0.0004680174,0.09487009,0.0005922007,0.0001406226,0.0004780436,0.00100493,0.0001206569,0.006178576],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008657272,"threshold_uncertainty_score":0.02711016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5673121605645557,"score_gpt":0.4621264805179012,"score_spread":0.1051856800466545,"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."}}