{"id":"W3012071714","doi":"10.5220/0008857302950302","title":"Advanced Analytics to Predict Survivability of Breast Cancer Patients","year":2020,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Survivability; Computer science; Breast cancer; Analytics; Cancer; Medicine; Data science; Internal medicine; Computer network","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002500741,0.0001167356,0.0002985618,0.00003718915,0.0001815923,0.000001746446,0.0002213587,0.00011779,0.002861533],"category_scores_gemma":[0.0007871114,0.00009871486,0.00005091036,0.0004783506,0.00004573647,0.00008544487,0.0001581382,0.0003128866,0.0002683471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001693276,"about_ca_system_score_gemma":0.0003417502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005617589,"about_ca_topic_score_gemma":0.005181807,"domain_scores_codex":[0.9979305,0.000252,0.0008067747,0.0003077769,0.0003103134,0.0003926179],"domain_scores_gemma":[0.9978039,0.0003701358,0.0001776991,0.0002772011,0.0009455833,0.0004254574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001742361,0.00005532747,0.9861766,0.000289228,0.000008570081,1.911625e-7,0.003086369,0.0003162115,0.0001156822,0.0002381294,0.002460192,0.007079271],"study_design_scores_gemma":[0.0002210876,0.0002887192,0.9844273,0.0001790384,0.00001649374,3.094568e-8,0.004993575,0.00524653,0.0008619865,0.0002390558,0.00333586,0.0001902683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813347,0.00001312228,0.0006184595,0.01307178,0.0005672324,0.001260376,0.0005536806,0.00009856938,0.002482116],"genre_scores_gemma":[0.9949397,0.00001530033,0.0003661258,0.004229243,0.000170777,0.0000814924,0.00001088835,0.00001820055,0.0001682423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01360507,"threshold_uncertainty_score":0.99805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1559165194323481,"score_gpt":0.4872915018489559,"score_spread":0.3313749824166078,"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."}}