{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002764675,0.0001217848,0.0002313752,0.0004665683,0.0008377697,0.0003153197,0.004546294,0.00005734786,0.0002113816],"category_scores_gemma":[0.00119629,0.0001144947,0.0001257393,0.001016589,0.0001380248,0.0005840638,0.0008372273,0.0004472707,0.00001996734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002437427,"about_ca_system_score_gemma":0.0002495768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002703415,"about_ca_topic_score_gemma":0.00006288179,"domain_scores_codex":[0.9961835,0.0001203552,0.001442014,0.000440781,0.001610522,0.0002028829],"domain_scores_gemma":[0.9948333,0.0007378826,0.001330509,0.0007901334,0.002223102,0.00008509542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009654676,0.0001652191,0.0008263672,0.000002259433,0.00004445982,0.000001736578,0.0001579331,0.09308926,0.00188228,0.1002786,0.0007266341,0.8027287],"study_design_scores_gemma":[0.00006890553,0.0001237726,0.0001881379,0.000009327702,0.00001967922,0.00008848519,0.005292367,0.117706,0.001282179,0.3047899,0.5702663,0.0001650018],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007179593,0.0003193905,0.9832578,0.006586808,0.000571328,0.0005338883,0.001472565,0.00003608411,0.00004254953],"genre_scores_gemma":[0.9614671,0.0001328208,0.03721066,0.0002156213,0.0004544253,0.0002239497,0.0002464233,0.00001701055,0.00003201262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9542875,"threshold_uncertainty_score":0.8448222,"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."}}