{"id":"W4308774387","doi":"10.2196/41342","title":"Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision Classification via Federated Learning: Model Development and Validation Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Taipei Veterans General Hospital; Far Eastern Memorial Hospital; Ministry of Science and Technology, Taiwan","keywords":"Computer science; Artificial intelligence; Natural language processing; Training (meteorology); Deep learning; Training set; Machine learning","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":[0.005737416,0.001876675,0.001287862,0.0009701022,0.0005763123,0.0009631541,0.001708007,0.001687588,0.001524216],"category_scores_gemma":[0.008161581,0.0004729822,0.001348712,0.0007176484,0.0005824946,0.001313446,0.001527383,0.00292953,0.0005301625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002120642,"about_ca_system_score_gemma":0.002708676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02563887,"about_ca_topic_score_gemma":0.01645781,"domain_scores_codex":[0.9985928,0.0006336237,0.00009563349,0.0003555096,0.0001376777,0.000184724],"domain_scores_gemma":[0.9943539,0.003342103,0.0002350861,0.0004731177,0.001459217,0.0001367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005667406,0.0008440275,0.01047883,0.0001228691,0.0002841147,0.0001437611,0.0001093891,0.8662614,0.001194823,0.0008436565,0.002086732,0.1170636],"study_design_scores_gemma":[0.00001531441,0.0000722047,0.0004357003,0.00001020488,0.00002263615,0.0000097406,0.00001743291,0.9982969,0.0006397293,0.0003963334,0.00007788902,0.000005821156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7877932,0.001846608,0.2015209,0.0009757409,0.0002372429,0.0003982103,0.001323609,0.003649188,0.002255291],"genre_scores_gemma":[0.9495664,0.0002161059,0.046836,0.0001934554,0.00002379114,0.0003146506,0.001527605,0.00005120835,0.001270896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02563887,"threshold_uncertainty_score":0.05097932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07201370155126448,"score_gpt":0.3714521018640488,"score_spread":0.2994384003127843,"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."}}