{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00107187,0.001069581,0.0006169475,0.001301843,0.0004604661,0.0005490507,0.001365566,0.000819116,0.00284787],"category_scores_gemma":[0.003055659,0.0003434498,0.0005242752,0.0007324179,0.000280574,0.0009053529,0.0008364015,0.001434592,0.001604826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355449,"about_ca_system_score_gemma":0.001666155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0163047,"about_ca_topic_score_gemma":0.01509355,"domain_scores_codex":[0.9993926,0.0001022425,0.00006676807,0.0002192663,0.0001250951,0.00009406661],"domain_scores_gemma":[0.9987772,0.0003696452,0.0001244564,0.0001477759,0.0004961402,0.00008482033],"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":[0.0008511621,0.001549287,0.01806889,0.0002582353,0.0001566508,0.0004314066,0.0002125553,0.1244565,0.01256859,0.001467188,0.02710666,0.8128728],"study_design_scores_gemma":[0.00002762369,0.00008490289,0.001833133,0.0000200882,0.00002055581,0.00007549504,0.00002689259,0.9900865,0.005822305,0.001027434,0.0009572253,0.00001782141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3905248,0.001347187,0.5487744,0.001509479,0.000726997,0.0008891051,0.005645874,0.04256894,0.008013244],"genre_scores_gemma":[0.7616982,0.0003834252,0.218966,0.0005723467,0.0001026046,0.0004742777,0.01054516,0.0002023818,0.007055605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0163047,"threshold_uncertainty_score":0.03241962,"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."}}