{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001353958,0.0003983726,0.0008805126,0.00006801632,0.000141227,0.00008140599,0.0009945319,0.0003034331,0.00002683955],"category_scores_gemma":[0.009313623,0.0003726209,0.0001698159,0.000118885,0.0001221587,0.00007005368,0.001226333,0.001104737,0.000001786711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007937712,"about_ca_system_score_gemma":0.0002768025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007459515,"about_ca_topic_score_gemma":0.00001554613,"domain_scores_codex":[0.9972855,0.00019699,0.0007339198,0.001023227,0.0003890849,0.0003713048],"domain_scores_gemma":[0.9959835,0.002254186,0.0001930616,0.001111705,0.0002617776,0.0001957491],"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":[0.001206695,0.001567291,0.008630591,0.0158157,0.003011477,0.00003601655,0.01238482,0.6320241,0.02945349,0.2798753,0.0005709211,0.01542353],"study_design_scores_gemma":[0.0003588123,0.00003771178,0.0002778838,0.0001760866,0.0004045857,0.000001200109,0.00003351835,0.7488002,0.00007280871,0.2495139,0.00003601427,0.0002873524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02896124,0.00001620318,0.9639825,0.0001890243,0.0001788555,0.0008645248,0.005518324,0.0001324381,0.000156909],"genre_scores_gemma":[0.305192,0.000007028843,0.6943024,0.00006047801,0.0001471784,0.00003851672,0.000168916,0.0000599007,0.00002357258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2762308,"threshold_uncertainty_score":0.9998726,"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."}}