{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002794213,0.0006118264,0.0006320992,0.001253056,0.0003153623,0.0005422183,0.0009219798,0.0006398067,0.0008261442],"category_scores_gemma":[0.0052057,0.0002366161,0.0004955759,0.0008507214,0.0002424352,0.0005807946,0.0004155154,0.0005637578,0.0002315784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008832066,"about_ca_system_score_gemma":0.002142045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01232755,"about_ca_topic_score_gemma":0.006829745,"domain_scores_codex":[0.999504,0.0002018089,0.00005433803,0.0001005594,0.0001026662,0.00003657761],"domain_scores_gemma":[0.9985429,0.0008595823,0.0001246172,0.00007220686,0.0003611119,0.00003952118],"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":[0.0002612197,0.0003450771,0.07631315,0.0002864259,0.0002611956,0.0001415296,0.0001274988,0.3209277,0.003458679,0.002415437,0.003174965,0.5922871],"study_design_scores_gemma":[0.0000277887,0.0000728203,0.005213129,0.00002539158,0.00003231141,0.00005132151,0.00002171217,0.9919245,0.001138491,0.0009173638,0.0005677321,0.000007489261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.310582,0.00350927,0.6785083,0.001500089,0.0001237761,0.0006021442,0.0004368731,0.001700198,0.003037327],"genre_scores_gemma":[0.6209271,0.001514523,0.3743581,0.0002935165,0.00007987305,0.0008377791,0.0006876618,0.00003938869,0.001261948],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01232755,"threshold_uncertainty_score":0.02451158,"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."}}