{"id":"W1971891529","doi":"10.1016/j.eswa.2012.01.105","title":"A scoring model to detect abusive billing patterns in health insurance claims","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Workplace Violence and Bullying","field":"Social Sciences","cited_by":93,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Hongik University","keywords":"Psychological intervention; Health care; Decision tree; Computer science; Quarter (Canadian coin); Categorization; Actuarial science; Intervention (counseling); Data mining; Medicine; Artificial intelligence; Nursing; Business","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":[0.002835299,0.0007422277,0.0009367419,0.001728222,0.0005065502,0.00114108,0.001299788,0.001148898,0.001745541],"category_scores_gemma":[0.007865595,0.0002837651,0.0007490274,0.001016219,0.0002548006,0.0008533379,0.0005057963,0.0009309425,0.0006210985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018113,"about_ca_system_score_gemma":0.001160787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02014674,"about_ca_topic_score_gemma":0.01779552,"domain_scores_codex":[0.9991632,0.0002514841,0.0001047143,0.0002093919,0.0001600551,0.000111236],"domain_scores_gemma":[0.9950669,0.00292249,0.0003067644,0.0001934564,0.001323176,0.0001871617],"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":[0.0008961141,0.00181456,0.1381739,0.0001361248,0.000408054,0.0003199234,0.0001723428,0.3414606,0.003540177,0.002230997,0.01120154,0.4996456],"study_design_scores_gemma":[0.000008434521,0.00005279138,0.004389969,0.000005840717,0.00002327536,0.0000257507,0.00001341677,0.9945071,0.0002282647,0.0006239859,0.0001145666,0.000006534609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5239797,0.0006149785,0.4643952,0.001477781,0.0002437806,0.0004524714,0.00205475,0.003799667,0.002981723],"genre_scores_gemma":[0.9428146,0.0001088014,0.05329175,0.0001455878,0.0000533262,0.0001850188,0.001148125,0.00003264114,0.002220242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02014674,"threshold_uncertainty_score":0.04005897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04169654788812277,"score_gpt":0.3407262214233228,"score_spread":0.2990296735352,"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."}}