{"id":"W3110333314","doi":"10.1109/dsaa49011.2020.00060","title":"Embedding for Anomaly Detection on Health Insurance Claims","year":2020,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Embedding; Context (archaeology); Health insurance; Computer science; Anomaly detection; Raw data; Data mining; Quality (philosophy); Business; Artificial intelligence; Health care; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0008841038,0.0006389727,0.0004775492,0.001321171,0.0002915396,0.0006771965,0.0004212387,0.0005621237,0.001339114],"category_scores_gemma":[0.006144864,0.0001552889,0.0003858401,0.0009258968,0.0004565737,0.001810221,0.001007456,0.001131234,0.0004233402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004542434,"about_ca_system_score_gemma":0.0004184786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001736247,"about_ca_topic_score_gemma":0.00197288,"domain_scores_codex":[0.9993178,0.0002384547,0.00005308763,0.0001529349,0.0001648659,0.00007288621],"domain_scores_gemma":[0.9972747,0.001443097,0.0003496821,0.0004035077,0.000422274,0.0001067608],"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":[0.0006434317,0.0005301489,0.03297257,0.0002492562,0.0001837574,0.000253307,0.0004196662,0.1449525,0.01180482,0.01485721,0.007248206,0.7858852],"study_design_scores_gemma":[0.000008462309,0.00008090474,0.004253272,0.00001933797,0.00001768684,0.00009163845,0.00007563313,0.9810112,0.002554104,0.01074781,0.001127002,0.00001293084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5503191,0.001737994,0.4411473,0.00117476,0.0001599646,0.00008115647,0.0008630126,0.001707393,0.002809281],"genre_scores_gemma":[0.9456464,0.0003747794,0.0511413,0.00007406149,0.00008215322,0.00003237826,0.001105162,0.00005918188,0.001484575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001736247,"threshold_uncertainty_score":0.004675627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04377232271479026,"score_gpt":0.3377549765826056,"score_spread":0.2939826538678154,"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."}}