{"id":"W3023815642","doi":"10.1007/978-3-030-47358-7_34","title":"Detection and Diagnosis of Breast Cancer Using a Bayesian Approach","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bayesian network; Breast cancer; Bayesian probability; Computer science; Conditional probability; Classifier (UML); Artificial intelligence; Predictive value; Sensitivity (control systems); Machine learning; Pattern recognition (psychology); Cancer; Algorithm; Statistics; Mathematics; Medicine; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001129926,0.0001611252,0.0002073699,0.00008832199,0.00005305986,0.00002346199,0.0002338369,0.0002598589,0.000003759705],"category_scores_gemma":[0.00003619698,0.0001370324,0.00004279304,0.00009908836,0.0006493853,0.000002703094,0.0002492016,0.0001731446,1.47e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002307915,"about_ca_system_score_gemma":0.0001153419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004659531,"about_ca_topic_score_gemma":0.00004292578,"domain_scores_codex":[0.9990083,0.00001155904,0.0001535602,0.0005129962,0.0001635514,0.0001500441],"domain_scores_gemma":[0.9995784,0.0000251934,0.00009812923,0.0001822208,0.00005055908,0.00006548312],"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":[0.0000224665,0.0000132564,0.001096934,0.00008749816,0.00002032316,0.000003836603,0.0001052328,0.00127766,0.02100798,0.00002137006,0.000004887871,0.9763386],"study_design_scores_gemma":[0.001850645,0.001858326,0.009296352,0.001812355,0.0002182172,0.0007507529,0.000006748711,0.6853219,0.2762491,0.01324079,0.006594465,0.002800394],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006514638,0.001541481,0.9912753,0.0001924905,0.0002011196,0.00009947129,0.00002077147,0.000009936867,0.0001448083],"genre_scores_gemma":[0.9420385,0.0002233423,0.05711249,0.0002712913,0.0003189725,0.000005741469,0.000003908302,0.0000136768,0.00001204127],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9735382,"threshold_uncertainty_score":0.5588017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899223615314027,"score_gpt":0.2565005234643172,"score_spread":0.237508287311177,"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."}}