{"id":"W2888789047","doi":"","title":"Toronto CL CLEF 2018 eHealth Task 1: Multi-lingual ICD-10 Coding using an Ensemble of Recurrent and Convolutional Neural Networks.","year":2018,"lang":"en","type":"article","venue":"CLEF (Working Notes)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Clef; Convolutional neural network; Computer science; eHealth; Coding (social sciences); Artificial intelligence; Speech recognition; Task (project management); Natural language processing; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004458313,0.002981734,0.001887212,0.005399346,0.001883008,0.002487368,0.002798186,0.003874714,0.02258476],"category_scores_gemma":[0.02198972,0.0007446322,0.001851454,0.002914494,0.001031867,0.001821406,0.003348527,0.003596018,0.01730075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005745175,"about_ca_system_score_gemma":0.007800506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1637793,"about_ca_topic_score_gemma":0.2427776,"domain_scores_codex":[0.9963014,0.00126033,0.000424288,0.0008519634,0.000804278,0.0003577749],"domain_scores_gemma":[0.9924608,0.003196829,0.0003483648,0.001299367,0.002107237,0.000587442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003751818,0.0001259503,0.003793987,0.0008247566,0.0001585105,0.0003645665,0.00007695422,0.00311567,0.002266828,0.0006320035,0.9301212,0.05814441],"study_design_scores_gemma":[0.002851802,0.0009533149,0.09512497,0.002728496,0.001349273,0.005159893,0.002220207,0.2364216,0.03836234,0.01829267,0.5956549,0.0008804429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.06277905,0.008655309,0.03263709,0.009468891,0.003834963,0.001889938,0.8481452,0.01378468,0.01880491],"genre_scores_gemma":[0.06105391,0.0009585207,0.02157593,0.0009851173,0.0003938012,0.0009276579,0.9045098,0.0008897972,0.008705352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1637793,"threshold_uncertainty_score":0.3256521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08263131018549892,"score_gpt":0.3588600078983651,"score_spread":0.2762286977128662,"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."}}