{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001107767,0.0003069803,0.0004091231,0.00008967119,0.0006640822,0.0001591473,0.0007154597,0.0002093976,0.00003832888],"category_scores_gemma":[0.0003029743,0.0003184911,0.00007858011,0.0002971154,0.0002613517,0.0004540623,0.0004916703,0.0004815612,0.000005799528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003187603,"about_ca_system_score_gemma":0.0001921195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001742738,"about_ca_topic_score_gemma":0.001539386,"domain_scores_codex":[0.996864,0.0004434397,0.0006401519,0.0008325698,0.0004340333,0.0007857553],"domain_scores_gemma":[0.9977124,0.0004822743,0.0004692082,0.0007138461,0.000278393,0.0003439135],"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":[0.000342041,0.0005870591,0.1628203,0.0004432224,0.00009322503,0.00004329991,0.01356368,0.02467601,0.002302697,0.01187424,0.0007551262,0.7824991],"study_design_scores_gemma":[0.0005272313,0.0004622136,0.02522811,0.0002507896,0.00001306909,0.00005153655,0.00005730554,0.9717025,0.000110872,0.0001136547,0.001161773,0.0003210055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6482323,0.002743822,0.3441347,0.0004028968,0.003634626,0.0004010009,0.000008260849,0.0003251131,0.0001172139],"genre_scores_gemma":[0.9445848,0.00005074541,0.05349062,0.0004014056,0.001400244,0.000004122507,0.00001447153,0.00003452299,0.00001908067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9470264,"threshold_uncertainty_score":0.9999267,"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."}}