{"id":"W4211190149","doi":"10.1016/j.jbi.2022.104008","title":"Privacy preserving collaborative learning of generalized linear mixed model","year":2022,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Differential privacy; Computer science; Homomorphic encryption; Usability; Encryption; Class (philosophy); Generalized linear model; Secure multi-party computation; Computation; Theoretical computer science; Data mining; Machine learning; Artificial intelligence; Computer security; Algorithm; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0120937,0.00160943,0.003951702,0.001300329,0.001504033,0.003342013,0.004816321,0.003133414,0.00255931],"category_scores_gemma":[0.03496323,0.001682248,0.003370317,0.001635147,0.00246182,0.005792539,0.007120623,0.004151129,0.0007925855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430083,"about_ca_system_score_gemma":0.002580166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003183774,"about_ca_topic_score_gemma":0.003305643,"domain_scores_codex":[0.9870412,0.008079484,0.0005514363,0.00211346,0.001551967,0.0006622729],"domain_scores_gemma":[0.9662247,0.02533878,0.001612376,0.004668197,0.001449502,0.0007065366],"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":[0.001036412,0.0004496449,0.003568259,0.0003385892,0.00063724,0.0003517839,0.0004680211,0.7668669,0.002189436,0.08770254,0.003756037,0.132635],"study_design_scores_gemma":[0.00002164547,0.0000674105,0.00009141618,0.000007934756,0.00002583494,0.00004157182,0.00001656634,0.9742199,0.0005170445,0.02471351,0.0002658071,0.00001151118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009470067,0.0002153674,0.9892537,0.0002471275,0.00002768635,0.00004434235,0.00008577866,0.0001868482,0.0004691957],"genre_scores_gemma":[0.6768395,0.0004973615,0.3149256,0.0004926951,0.0002701655,0.000465999,0.001075039,0.0001842903,0.005249381],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0120937,"threshold_uncertainty_score":0.06395841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03027644141557115,"score_gpt":0.2919081236792762,"score_spread":0.261631682263705,"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."}}