{"id":"W2515500000","doi":"10.1186/s12911-016-0348-6","title":"Development and validation of method for defining conditions using Chinese electronic medical record","year":2016,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Electronic medical record; Medical record; Data extraction; Health informatics; Chart; Beijing; Information extraction; Gold standard (test); Predictive value; MEDLINE; Family medicine; China; Internal medicine; Computer science; Pathology; Information retrieval; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05489999,0.001851889,0.001334776,0.01800943,0.001440264,0.003454041,0.002000698,0.001262828,0.002636162],"category_scores_gemma":[0.1146663,0.0007046712,0.002812925,0.00822238,0.001011561,0.003551858,0.002434378,0.001306704,0.001140273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002131463,"about_ca_system_score_gemma":0.008215026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007240993,"about_ca_topic_score_gemma":0.006020781,"domain_scores_codex":[0.9533479,0.01724486,0.01365232,0.005941488,0.009113449,0.0007000634],"domain_scores_gemma":[0.8766328,0.06585959,0.01099322,0.009218512,0.03648658,0.0008092517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003959438,0.0005001485,0.5045361,0.003164145,0.0007297125,0.0004752117,0.003179768,0.003692845,0.004503369,0.004909551,0.01249695,0.4614162],"study_design_scores_gemma":[0.001089008,0.001149462,0.6690822,0.003867102,0.002177862,0.002544274,0.007452069,0.1772786,0.03726368,0.0199319,0.07748346,0.0006804055],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1916863,0.002340221,0.7432542,0.002044125,0.0006919643,0.02239176,0.02344973,0.002998914,0.01114285],"genre_scores_gemma":[0.2177699,0.0007696697,0.7566652,0.0003212157,0.0001562115,0.01259201,0.01076637,0.0001306321,0.000828827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05489999,"threshold_uncertainty_score":0.2903424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04592820305339827,"score_gpt":0.4103737253728761,"score_spread":0.3644455223194778,"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."}}