{"id":"W3016759068","doi":"10.2196/17364","title":"Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Breast cancer; Random forest; Artificial intelligence; Logistic regression; Receiver operating characteristic; Algorithm; Artificial neural network; Medicine; Computer science; Area under curve; Cancer; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005450815,0.000179646,0.0002397712,0.0001302359,0.0001235426,0.00008619085,0.0006379657,0.0001482541,0.0001048819],"category_scores_gemma":[0.00007790264,0.0001529185,0.00002758732,0.0008825269,0.00004504472,0.0008196764,0.0005156416,0.0007476508,0.00001269822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005811765,"about_ca_system_score_gemma":0.0004778189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008122071,"about_ca_topic_score_gemma":0.00002371366,"domain_scores_codex":[0.997842,0.00004400862,0.0006968813,0.0001614559,0.0008511379,0.0004044823],"domain_scores_gemma":[0.9991628,0.00004907237,0.0002218637,0.0001560728,0.00005862165,0.0003516024],"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":[0.00001039206,0.00003326495,0.02417702,0.0001987064,0.00002047701,0.00001699328,0.06189764,0.0004598503,0.00004972126,0.00001182631,0.00002495866,0.9130992],"study_design_scores_gemma":[0.0003399639,0.00004457773,0.001780859,0.0001715881,0.0000011146,0.00006030975,0.0005351421,0.9941911,0.0002859687,0.00002235261,0.002382937,0.0001840568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3046678,0.00004535836,0.6928104,0.001024028,0.0002430468,0.0003540092,0.000006063327,0.0006274451,0.0002218376],"genre_scores_gemma":[0.4825992,0.000112122,0.5116988,0.004442472,0.0006544399,0.0004123554,0.000007802018,0.00004147052,0.00003126714],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9937313,"threshold_uncertainty_score":0.6235834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124790420262643,"score_gpt":0.2835731275679739,"score_spread":0.2710940855417096,"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."}}