{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004423223,0.0002117842,0.000261872,0.0001176856,0.0002332801,0.0002320467,0.005251172,0.0002440435,0.000003659373],"category_scores_gemma":[0.0005637123,0.0002070842,0.0001321029,0.000438774,0.0001198791,0.0005528044,0.0003311453,0.000491509,0.00002814625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004649892,"about_ca_system_score_gemma":0.0001143087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007547421,"about_ca_topic_score_gemma":0.000009842233,"domain_scores_codex":[0.9979241,0.0001209538,0.0004660163,0.0008954619,0.0003127307,0.0002807125],"domain_scores_gemma":[0.9950283,0.001879342,0.0001594496,0.002622138,0.0001053096,0.0002054376],"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":[0.0004125919,0.0003364653,0.00005462284,0.0001489339,0.00009729412,0.00002265472,0.0001448936,0.003126774,0.0003123366,0.001184846,0.08011165,0.9140469],"study_design_scores_gemma":[0.0009417264,0.0004457026,0.0001423051,0.00007473154,0.00002381881,0.000009976702,0.00001939623,0.9853621,0.001379551,0.006718158,0.004669281,0.000213276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00320978,0.0000137415,0.8949921,0.0991182,0.001394969,0.0003912298,0.00003274528,0.000835581,0.00001162635],"genre_scores_gemma":[0.2788406,0.00004133285,0.7181951,0.002805255,0.00008494126,0.00001128275,0.000005332971,0.00001483978,0.000001224783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9822353,"threshold_uncertainty_score":0.9758072,"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."}}