{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000220773,0.000093537,0.0001339373,0.00004381264,0.0002101315,0.0000666434,0.0003650768,0.00003794014,0.000006898183],"category_scores_gemma":[0.0001221865,0.00008616866,0.00004630891,0.0002682389,0.00000725778,0.0001633697,0.00006016867,0.0001876106,0.00004297346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005948431,"about_ca_system_score_gemma":0.00006899338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001272455,"about_ca_topic_score_gemma":0.0000294018,"domain_scores_codex":[0.99892,0.00008331895,0.0001957683,0.0003837678,0.0001584757,0.0002586709],"domain_scores_gemma":[0.9993019,0.0001466653,0.00009231481,0.000243636,0.00004695122,0.0001684996],"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":[0.00004913087,0.00002840432,0.008382359,0.0003203695,0.000007906882,0.000002534839,0.00209189,0.01015656,0.0003217673,0.02740243,0.001881103,0.9493555],"study_design_scores_gemma":[0.0002742046,0.00106113,0.01195008,0.00002097607,2.959778e-7,0.000003648035,0.00002200143,0.9742774,0.001217672,0.0006401078,0.01040334,0.0001291563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03697177,0.0000282076,0.915325,0.04611171,0.0003194312,0.0003386819,0.000002067171,0.0004669573,0.0004361081],"genre_scores_gemma":[0.9243762,0.000004590637,0.06154964,0.01381859,0.0001540997,0.000026266,0.000001113439,0.000009745759,0.00005971752],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9641208,"threshold_uncertainty_score":0.3513856,"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."}}