{"id":"W2890258652","doi":"10.2196/11361","title":"Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Machine learning; 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":[],"consensus_categories":[],"category_scores_codex":[0.003900993,0.0002656647,0.0007933707,0.0002874863,0.0002656116,0.0001148937,0.000278993,0.00007576158,0.00001272064],"category_scores_gemma":[0.0008533439,0.0002332117,0.00002060972,0.0008304077,0.0001532129,0.0003833057,0.0003008264,0.0004307113,0.00001201382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002184206,"about_ca_system_score_gemma":0.000711223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001737442,"about_ca_topic_score_gemma":0.0005027851,"domain_scores_codex":[0.9962311,0.001166093,0.0006789207,0.000764946,0.0004830814,0.0006758326],"domain_scores_gemma":[0.9975916,0.0002576976,0.0003620562,0.0008985784,0.0002675419,0.0006224978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002445773,0.0006930139,0.9383646,0.0001227195,0.00004094711,0.00001414266,0.001391546,0.000002837903,0.00004226306,0.00000391891,0.00008493022,0.05899456],"study_design_scores_gemma":[0.003775378,0.00217742,0.9562863,0.00007370891,0.000002805792,0.00001308981,0.0009872469,0.02373273,0.000001776601,0.000002578778,0.01269525,0.0002517655],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958367,0.0006211326,0.0006992611,0.0007483402,0.0002342998,0.001351097,0.0000671177,0.0002073295,0.0002347667],"genre_scores_gemma":[0.9927374,0.00007550199,0.006127178,0.000146955,0.0003447616,0.00003793415,0.0004473263,0.00004556049,0.00003738374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0587428,"threshold_uncertainty_score":0.9510096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078165834541684,"score_gpt":0.425540823936369,"score_spread":0.3177242404822006,"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."}}