{"id":"W3172809435","doi":"10.2196/26598","title":"Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Korea Health Industry Development Institute; National Research Foundation","keywords":"Autoencoder; Computer science; Generalizability theory; Artificial intelligence; Machine learning; Raw data; Categorical variable; Deep learning; Artificial neural network; Data mining; Feature (linguistics); Data modeling; Pattern recognition (psychology); Database; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.007966858,0.001183551,0.0008515506,0.0006013703,0.0005884974,0.001439162,0.001508717,0.001310571,0.001994927],"category_scores_gemma":[0.02025674,0.0004766342,0.000689219,0.0007367409,0.0009512065,0.003098714,0.001972152,0.001603859,0.000465216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408727,"about_ca_system_score_gemma":0.001926225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038049,"about_ca_topic_score_gemma":0.005439675,"domain_scores_codex":[0.9972894,0.00123024,0.0001527469,0.0004778083,0.0005549446,0.0002948719],"domain_scores_gemma":[0.9874334,0.006877045,0.0004283111,0.002922765,0.002166882,0.0001716698],"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":[0.0005707023,0.0007351575,0.01502145,0.0002062794,0.0001874709,0.0002497546,0.0001491024,0.7053114,0.005682435,0.01190788,0.002016528,0.2579618],"study_design_scores_gemma":[0.00002263386,0.000140212,0.0006782516,0.00002029326,0.00001801293,0.00004202575,0.00004994828,0.9918543,0.002671229,0.004147483,0.0003485932,0.000006880243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3621117,0.001139034,0.6273206,0.001248411,0.0001102316,0.0002756176,0.0001682546,0.002550292,0.005075739],"genre_scores_gemma":[0.9111869,0.0002957915,0.08732864,0.0001149646,0.0000272113,0.0001027306,0.0001616776,0.00005574564,0.0007263267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01038049,"threshold_uncertainty_score":0.04213327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06282766158257184,"score_gpt":0.3817565824914482,"score_spread":0.3189289209088763,"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."}}