{"id":"W4404864108","doi":"10.1111/trf.18077","title":"Privacy‐preserving federated data access and federated learning: Improved data sharing and <scp>AI</scp> model development in transfusion medicine","year":2024,"lang":"en","type":"review","venue":"Transfusion","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; Canadian Blood Services; McMaster University; Héma-Québec; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Canadian Blood Services","keywords":"Health care; Computer science; Big data; Data governance; Data sharing; Standardization; Transformative learning; Information privacy; Corporate governance; Personalized medicine; Precision medicine; Resource allocation; Data management; Data science; Data mining; Business; Computer security; Data quality; Medicine; Operations management; Engineering","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":[0.02278058,0.0007269734,0.001431397,0.001468701,0.001274422,0.005416167,0.003537875,0.001859169,0.001990711],"category_scores_gemma":[0.02914996,0.0005505285,0.002365109,0.002716294,0.002687403,0.009129023,0.006769402,0.003840106,0.0004957363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003475584,"about_ca_system_score_gemma":0.006449642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006374524,"about_ca_topic_score_gemma":0.003465086,"domain_scores_codex":[0.9864354,0.006869321,0.0009319878,0.001979551,0.002954768,0.0008289804],"domain_scores_gemma":[0.9780101,0.008910081,0.001262384,0.007712117,0.003336625,0.0007686791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005213882,0.0003584067,0.003915625,0.0002362696,0.0002041927,0.0002976678,0.0005414346,0.5729892,0.001745169,0.2292203,0.006240581,0.1837298],"study_design_scores_gemma":[0.00002057923,0.00004705748,0.0002084938,0.00004556537,0.00001651991,0.00005572998,0.00007297315,0.8670324,0.001232818,0.1279266,0.00332676,0.00001444475],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01299047,0.0004886822,0.9808952,0.002530762,0.0000759892,0.0001415591,0.0002350676,0.0006323451,0.002009853],"genre_scores_gemma":[0.526547,0.0009782048,0.467293,0.001048327,0.0001749758,0.0003227387,0.001063719,0.0001521529,0.002419868],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02278058,"threshold_uncertainty_score":0.1204767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1796688610430206,"score_gpt":0.3812968907245141,"score_spread":0.2016280296814935,"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."}}