{"id":"W3158874880","doi":"10.1016/j.artmed.2021.102077","title":"Fenchel duality of Cox partial likelihood with an application in survival kernel learning","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Dental and Craniofacial Research; National Cancer Institute; National Institutes of Health; American Cancer Society","keywords":"Computer science; Kernel (algebra); Duality (order theory); Proportional hazards model; Convex optimization; Mathematical optimization; Likelihood function; Regularization (linguistics); Artificial intelligence; Machine learning; Regular polygon; Algorithm; Mathematics; Statistics; Estimation theory","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009000354,0.0008780567,0.00154772,0.001339695,0.0006420378,0.001589368,0.001226463,0.001350368,0.002375819],"category_scores_gemma":[0.0237025,0.0005125282,0.001359793,0.001523199,0.002494815,0.00203752,0.002984157,0.003065991,0.000522139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001309218,"about_ca_system_score_gemma":0.002582378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001559039,"about_ca_topic_score_gemma":0.0008294102,"domain_scores_codex":[0.9973303,0.001890411,0.00008548956,0.0001796799,0.0003757694,0.0001382915],"domain_scores_gemma":[0.9907195,0.006871487,0.0004792197,0.0005825158,0.001030409,0.0003168249],"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":[0.0001751328,0.0001490159,0.002410145,0.0002455245,0.00008161504,0.0002462199,0.0002182956,0.3992386,0.001388053,0.5291824,0.004189701,0.06247533],"study_design_scores_gemma":[0.0000208478,0.00005353405,0.0002212222,0.00002120603,0.000008371107,0.00008308835,0.00002800395,0.8698291,0.0005576406,0.1273392,0.00182289,0.00001503089],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00596645,0.0004231992,0.99168,0.0004737044,0.00004114593,0.00003196866,0.00003960831,0.00005881036,0.001285162],"genre_scores_gemma":[0.4568993,0.002337555,0.5320932,0.0006712641,0.0003296415,0.0004860075,0.0003962882,0.0002651487,0.006521617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009000354,"threshold_uncertainty_score":0.04759896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1623907024273834,"score_gpt":0.4396824087499342,"score_spread":0.2772917063225508,"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."}}