{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005056356,0.001669941,0.00138742,0.005496696,0.0007861328,0.00149119,0.002212284,0.002020289,0.002209771],"category_scores_gemma":[0.01156032,0.000394313,0.002071524,0.002591072,0.0005836068,0.002070663,0.001284534,0.00218361,0.0007348428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003057568,"about_ca_system_score_gemma":0.002788538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06645738,"about_ca_topic_score_gemma":0.04178643,"domain_scores_codex":[0.9986736,0.0004898431,0.0001181878,0.000315962,0.000272588,0.0001297806],"domain_scores_gemma":[0.9917258,0.006104969,0.0003068745,0.0004614427,0.001191902,0.000208891],"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":[0.0009466991,0.001341061,0.02855171,0.0004241275,0.0005302767,0.0003157841,0.0001410471,0.6337066,0.001080755,0.002741257,0.01105767,0.319163],"study_design_scores_gemma":[0.00001583687,0.00003643063,0.0007508281,0.00001409579,0.00003282747,0.00001885907,0.00001200263,0.9979277,0.0001722867,0.0007490579,0.0002654436,0.000004758412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5515794,0.01264062,0.393088,0.006898612,0.0006989362,0.00178412,0.01042462,0.01403821,0.008847553],"genre_scores_gemma":[0.7892337,0.002148317,0.1925766,0.000752393,0.000211396,0.0005715641,0.01196698,0.0001301514,0.002408951],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06645738,"threshold_uncertainty_score":0.1321411,"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."}}