{"id":"W3165172487","doi":"10.3233/shti210147","title":"Federated Deep Learning Architecture for Personalized Healthcare","year":2021,"lang":"en","type":"book-chapter","venue":"Studies in health technology and informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Mitacs; F. Hoffmann-La Roche","keywords":"Computer science; Deep learning; Architecture; Face (sociological concept); Federated learning; Deep neural networks; Data science; Health care; Artificial intelligence; Artificial neural network; Big data; Information privacy; Machine learning; Computer security; Data mining; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0008631455,0.000485083,0.001146752,0.001147659,0.0007817244,0.00007162839,0.00483,0.001063156,0.000003673226],"category_scores_gemma":[0.01079021,0.0004638624,0.00007999172,0.000426278,0.0008959824,0.0002389572,0.03021142,0.002277372,0.000004524422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003569289,"about_ca_system_score_gemma":0.0003104174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006202287,"about_ca_topic_score_gemma":0.0001065142,"domain_scores_codex":[0.9971626,0.00004525015,0.001280674,0.0005007313,0.0002526926,0.0007580735],"domain_scores_gemma":[0.9961157,0.0004843907,0.0008303403,0.002169108,0.0003265891,0.000073811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001513413,0.0000107698,0.00007358177,0.004893392,0.0001795231,0.00002038531,0.001794231,0.000008838252,2.159195e-7,0.6184469,0.01244751,0.3621095],"study_design_scores_gemma":[0.0006587823,0.0004807378,0.000004184646,0.001781847,0.000009653698,0.0001325923,0.00290539,0.01257464,0.000008256206,0.6166936,0.3642735,0.0004768293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001134624,0.2549789,0.3947769,0.3294652,0.0017516,0.003273016,0.000127195,0.003843167,0.01167056],"genre_scores_gemma":[0.001448597,0.1391436,0.8397771,0.004735506,0.00008640814,0.0004306804,0.0002106999,0.00009339528,0.01407402],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4450002,"threshold_uncertainty_score":0.9997813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06651754710366126,"score_gpt":0.3443571886929163,"score_spread":0.2778396415892551,"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."}}