{"id":"W3082110198","doi":"10.1109/tc.2020.3020545","title":"Practical and Secure SVM Classification for Cloud-Based Remote Clinical Decision Services","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Queen's University; University of Waterloo","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Notation; Support vector machine; Cloud computing; Computer science; Leverage (statistics); Machine learning; Clinical decision support system; Classifier (UML); Artificial intelligence; Algorithm; Decision support system; Data mining; Mathematics","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":[0.001639993,0.0005875399,0.0008022434,0.0004030533,0.0008680124,0.001914083,0.001052625,0.0009615614,0.004136804],"category_scores_gemma":[0.006851668,0.0002464781,0.0006151393,0.0006432992,0.0006979802,0.002541041,0.002273144,0.001738288,0.001560391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382675,"about_ca_system_score_gemma":0.002355862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001540582,"about_ca_topic_score_gemma":0.001161859,"domain_scores_codex":[0.997068,0.0007136961,0.0003215703,0.0004285261,0.001003304,0.0004649881],"domain_scores_gemma":[0.9970782,0.0009152534,0.0003818729,0.0009846571,0.0004878317,0.0001522093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00275209,0.0006188615,0.01255244,0.0005930586,0.0002057521,0.001584952,0.000563418,0.2593829,0.03945487,0.1488858,0.03117068,0.5022352],"study_design_scores_gemma":[0.00006461638,0.00009543118,0.0007662661,0.00003278452,0.00002195015,0.0003944528,0.0000672574,0.9557847,0.01216964,0.02546065,0.005120205,0.00002199565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06163758,0.0008294686,0.9267633,0.002214301,0.0002306365,0.0002941096,0.0004792517,0.001971492,0.0055799],"genre_scores_gemma":[0.8928571,0.0004438974,0.1031029,0.0003863931,0.0001203115,0.0001908054,0.0005397933,0.0001023491,0.00225645],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004136804,"threshold_uncertainty_score":0.01383901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004054206705346,"score_gpt":0.3646781160977232,"score_spread":0.2642726954271887,"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."}}