{"id":"W3169323730","doi":"10.1007/s11042-021-11114-5","title":"Predicting breast cancer biopsy outcomes from BI-RADS findings using random forests with chi-square and MI features","year":2021,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Random forest; Breast cancer; Computer science; Mammography; Classifier (UML); Biopsy; BI-RADS; Feature selection; Artificial intelligence; Estimator; Medicine; Machine learning; Breast cancer screening; Cancer; Radiology; Pattern recognition (psychology); Statistics; Internal medicine; Mathematics","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":[0.00006189051,0.0001511017,0.0001836227,0.00004839192,0.0003360877,0.000299736,0.0001655404,0.00007322706,0.00001353685],"category_scores_gemma":[0.00001675069,0.00012484,0.0000308832,0.0003124092,0.00008206158,0.0003696496,0.0001324401,0.0001382858,0.000001302431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004272215,"about_ca_system_score_gemma":0.00007779293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007185164,"about_ca_topic_score_gemma":0.0005660513,"domain_scores_codex":[0.9989839,0.00002276215,0.0001568418,0.0004741751,0.0001720208,0.0001903677],"domain_scores_gemma":[0.9992377,0.0001773666,0.00008240093,0.0003102304,0.0000854344,0.0001069306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003137254,0.00005107504,0.7156472,0.00004039261,0.0001035942,0.000006662719,0.001209321,0.0003325189,0.004984799,0.0002270761,0.00007753527,0.2772884],"study_design_scores_gemma":[0.00142959,0.00001121627,0.9382469,0.00009178265,0.00004924835,0.00009859809,0.0001106177,0.05662349,0.002452447,0.0002617417,0.0004060355,0.0002183091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6243889,0.001166828,0.3714737,0.001759156,0.0001476413,0.0004901469,0.000352027,0.0001538765,0.00006769298],"genre_scores_gemma":[0.9438483,0.0001180248,0.05523926,0.0001258063,0.0002219143,0.0003646723,0.00002619841,0.00001701621,0.00003878138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3194594,"threshold_uncertainty_score":0.5090827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907542134954093,"score_gpt":0.2658236636203795,"score_spread":0.2467482422708386,"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."}}