{"id":"W4293070708","doi":"10.5121/ijaia.2022.13305","title":"Data Standardization using Deep Learning for Healthcare Insurance Claims","year":2022,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Standardization; Computer science; Metadata; Receipt; Deep learning; Task (project management); Data mining; Artificial intelligence; Data science; Machine learning; Information retrieval; World Wide Web; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.00283227,0.0006887649,0.0007404668,0.001621251,0.0004273892,0.001349671,0.001302028,0.001172294,0.002171607],"category_scores_gemma":[0.006749755,0.0003983361,0.001018835,0.001926708,0.0005391388,0.001794826,0.001734286,0.002327315,0.0009510639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001703261,"about_ca_system_score_gemma":0.00166395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01132539,"about_ca_topic_score_gemma":0.01119129,"domain_scores_codex":[0.9990122,0.0002164212,0.0001054265,0.0002616512,0.0002814922,0.0001227166],"domain_scores_gemma":[0.9982943,0.0005852485,0.0001963336,0.0003999308,0.0004440797,0.0000800947],"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":[0.0003307799,0.0005155403,0.01400589,0.0001353196,0.0001803021,0.0001869772,0.0001699502,0.3899929,0.003250497,0.008723771,0.01026695,0.5722412],"study_design_scores_gemma":[0.00001101931,0.00003984897,0.001241012,0.00001994326,0.00001188379,0.00002239317,0.00002882459,0.9884393,0.002008361,0.006419089,0.001748899,0.000009487174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1786611,0.002464121,0.7995511,0.003085279,0.0003159175,0.0002715342,0.002588655,0.007046683,0.006015655],"genre_scores_gemma":[0.8224509,0.0006603241,0.1640727,0.0004511164,0.0001320765,0.0002138145,0.006512956,0.0001592003,0.005346907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01132539,"threshold_uncertainty_score":0.02251893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4530248982000601,"score_gpt":0.497278961173409,"score_spread":0.04425406297334894,"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."}}