{"id":"W2906278637","doi":"10.1111/deci.12348","title":"Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions","year":2018,"lang":"en","type":"article","venue":"Decision Sciences","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Decision tree; Artificial intelligence; Classifier (UML); Machine learning; Naive Bayes classifier; Support vector machine; Logistic regression; Bayes classifier; Random forest; Pattern recognition (psychology); Algorithm; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00263241,0.0008495811,0.001116552,0.001340898,0.000477488,0.001364518,0.001496545,0.001216147,0.004332481],"category_scores_gemma":[0.009968034,0.000512508,0.000776175,0.0009758445,0.0005633598,0.001229162,0.00123508,0.002108062,0.00116352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001101641,"about_ca_system_score_gemma":0.002419386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00585279,"about_ca_topic_score_gemma":0.003977318,"domain_scores_codex":[0.9988505,0.0003979126,0.00009395777,0.0002380075,0.0003160001,0.0001036022],"domain_scores_gemma":[0.9968539,0.001978115,0.0002219129,0.0001293522,0.0007069624,0.000109832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000288768,0.0002916654,0.003517895,0.0001056035,0.0000742537,0.0001603024,0.0000889265,0.563773,0.002421333,0.01561416,0.008064892,0.4055991],"study_design_scores_gemma":[0.00000849262,0.00001698628,0.00009885968,0.000003373092,0.000002660779,0.00001145983,0.000005379828,0.9965103,0.0003360012,0.002744789,0.000258931,0.000002663284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009520462,0.00007560612,0.9884456,0.0003915043,0.00002373917,0.00008173739,0.00006815355,0.000598766,0.0007945386],"genre_scores_gemma":[0.2343934,0.0001352555,0.7610466,0.0003733423,0.00009945834,0.0005091366,0.0005027358,0.000177547,0.00276252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00585279,"threshold_uncertainty_score":0.01449358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04055952601994985,"score_gpt":0.3685972533458382,"score_spread":0.3280377273258884,"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."}}