{"id":"W3031933442","doi":"10.2196/17653","title":"Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Data science; Medical waste; Computer security; Data mining; Medical emergency; Risk analysis (engineering); Knowledge management; Business; Medicine; Engineering","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":[0.001361135,0.0002328723,0.0002584972,0.0001233627,0.0003296043,0.0000282241,0.0003778322,0.0003871653,0.0002415275],"category_scores_gemma":[0.0002934564,0.0002057316,0.00002412531,0.0003098567,0.0001694894,0.0005496421,0.00025912,0.001180414,0.0002176594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006408832,"about_ca_system_score_gemma":0.0004161899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.663389e-7,"about_ca_topic_score_gemma":0.00001346467,"domain_scores_codex":[0.9978034,0.0001104584,0.0006180707,0.0002694554,0.0008378203,0.0003607717],"domain_scores_gemma":[0.9981372,0.0002199449,0.0001308709,0.0002049387,0.0001042357,0.001202833],"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":[0.0004299028,0.0003059278,0.0004321093,0.0004383445,0.0001325755,0.00000879751,0.02759024,0.001657349,0.0005483003,0.00006535038,0.01130415,0.957087],"study_design_scores_gemma":[0.001252597,0.0001225746,0.0001555254,0.000102294,0.00006051223,0.00001830006,0.0008528393,0.8864234,0.01087401,0.00001241549,0.09988291,0.0002426014],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908935,0.0001281912,0.003939682,0.001516587,0.0003815248,0.0006490884,0.00002821413,0.0001011357,0.002362007],"genre_scores_gemma":[0.9835817,0.001170845,0.000516952,0.01411034,0.0002998604,0.0001695702,0.00008079649,0.00001512145,0.00005480366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9568443,"threshold_uncertainty_score":0.8389491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1515249600550641,"score_gpt":0.4736024870147421,"score_spread":0.322077526959678,"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."}}