{"id":"W3198282417","doi":"10.2196/23230","title":"Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Computer science; Coding (social sciences); Artificial intelligence; Deep learning; Encoder; Artificial neural network; Medical diagnosis; Natural language processing; Medical classification; Machine learning; Language model; Autoencoder; ICD-10; Speech recognition; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003182166,0.000244234,0.0005738199,0.0001321642,0.001447255,0.00005183149,0.000196532,0.0005516251,0.003516628],"category_scores_gemma":[0.002387139,0.0002014525,0.000073035,0.0004227782,0.0001003697,0.0002826063,0.0001305894,0.002169149,0.0004454574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001944113,"about_ca_system_score_gemma":0.0008793746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006576281,"about_ca_topic_score_gemma":0.00001068744,"domain_scores_codex":[0.9950857,0.0006505788,0.001852922,0.0001470431,0.001304696,0.0009591118],"domain_scores_gemma":[0.9957448,0.002302511,0.0004842668,0.0002830215,0.0002023706,0.0009830124],"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":[0.000251901,0.0001713193,0.01365824,0.05453917,0.0001086413,0.0001989832,0.1871388,0.007743313,0.000004915772,0.01530793,0.04428297,0.6765939],"study_design_scores_gemma":[0.001687275,0.0001346458,0.001306717,0.00326352,0.00001999789,0.00002097573,0.03012036,0.9512517,8.58133e-7,0.00001983981,0.01198454,0.0001895978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8079873,0.000233419,0.03980399,0.00928612,0.003475177,0.002486408,0.00001417962,0.00220641,0.134507],"genre_scores_gemma":[0.9781799,0.0000318153,0.004118003,0.01609817,0.0007776066,0.0002118475,0.0001743905,0.00003076993,0.0003775011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9435084,"threshold_uncertainty_score":0.9998527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1282203436536169,"score_gpt":0.4106309465578995,"score_spread":0.2824106029042825,"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."}}