{"id":"W4313452912","doi":"10.1109/bibm55620.2022.9995700","title":"Private Federated Framework for Health Data","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; University of Manitoba","funders":"","keywords":"Differential privacy; Computer science; Raw data; Architecture; Federated learning; Information privacy; Private information retrieval; Layer (electronics); Information sensitivity; Data modeling; Noise (video); Data mining; Computer security; Distributed computing; Artificial intelligence; Database","routes":{"ca_aff":true,"ca_fund":false,"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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001086488,0.0001990003,0.0002586133,0.0004374864,0.0004730854,0.0003063383,0.01620391,0.0000667258,0.0001404565],"category_scores_gemma":[0.002352039,0.0001721182,0.00003005352,0.0005391717,0.0001258561,0.0005745872,0.02916514,0.0004554503,0.00001130734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001359933,"about_ca_system_score_gemma":0.0001987932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002656795,"about_ca_topic_score_gemma":0.000004147176,"domain_scores_codex":[0.9978006,0.00004004781,0.0005813589,0.0004551935,0.0007783396,0.0003443944],"domain_scores_gemma":[0.9961464,0.0002136915,0.0004034495,0.003008122,0.0001140735,0.0001142551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006730668,0.0001529878,0.00008611859,0.00009687875,0.0001209836,0.00000878498,0.0003080852,0.00000627433,0.0002300092,0.2557176,0.6089492,0.1342558],"study_design_scores_gemma":[0.0005386294,0.0005590191,0.0000642661,0.00008096023,0.000003859731,0.00002983741,0.0002990443,0.8148949,0.0001039908,0.128757,0.05447659,0.0001919681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0011421,0.00007660126,0.7804096,0.2129916,0.002201351,0.0004722002,0.001546122,0.0002848328,0.0008755374],"genre_scores_gemma":[0.1571212,0.001188957,0.8235603,0.01450302,0.0002574997,0.0001855894,0.00294721,0.00002652583,0.0002096836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8148886,"threshold_uncertainty_score":0.9891189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.130495123056648,"score_gpt":0.372480963103445,"score_spread":0.241985840046797,"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."}}