{"id":"W1555474281","doi":"","title":"Electronic Fraud Detection in the U.S. Medicaid Healthcare Program: Lessons Learned from other Industries","year":2011,"lang":"en","type":"article","venue":"University of Twente Research Information","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicaid; Health care; Business; Government (linguistics); Quarter (Canadian coin); Actuarial science; Finance; Economics; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02146739,0.0004998797,0.0007603503,0.009835126,0.00128866,0.003344959,0.001143366,0.001854484,0.001083704],"category_scores_gemma":[0.08044191,0.0002642311,0.0009484703,0.01123567,0.001837911,0.005565949,0.001181854,0.001486372,0.0001937951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00299157,"about_ca_system_score_gemma":0.003831137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01811393,"about_ca_topic_score_gemma":0.02187499,"domain_scores_codex":[0.9870697,0.007421471,0.0008294198,0.0005002666,0.003799344,0.0003797116],"domain_scores_gemma":[0.9160928,0.0641232,0.004294678,0.002657275,0.01219323,0.0006388911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001304264,0.0007440217,0.09828745,0.001083463,0.0001990664,0.0002569231,0.001067494,0.00386851,0.0002154059,0.01508492,0.0078532,0.8712091],"study_design_scores_gemma":[0.0004544927,0.00216281,0.391359,0.01798077,0.001163911,0.001582455,0.02300619,0.1376378,0.009657132,0.2484877,0.166109,0.0003987021],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4861176,0.2624489,0.05407079,0.1545556,0.0006438352,0.0009386747,0.001521074,0.0002210959,0.03948252],"genre_scores_gemma":[0.7539817,0.175165,0.05969214,0.007223211,0.0006854525,0.0002803867,0.0009612659,0.00003223572,0.001978613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02146739,"threshold_uncertainty_score":0.1135318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2064832985060862,"score_gpt":0.3585661105535196,"score_spread":0.1520828120474334,"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."}}