{"id":"W1270312698","doi":"10.1007/s13721-015-0090-5","title":"Mining clinical text for stroke prediction","year":2015,"lang":"en","type":"article","venue":"Network Modeling Analysis in Health Informatics and Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Island Health; University of Victoria","funders":"","keywords":"Triage; Computer science; Health informatics; Support vector machine; Stroke (engine); Artificial intelligence; Machine learning; Natural language processing; Data science; Data mining; Medicine; Medical emergency; Public health; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004282728,0.0001933374,0.0005232897,0.0002084673,0.0001522767,0.00006467036,0.000169263,0.0003132521,8.539417e-7],"category_scores_gemma":[0.0004150171,0.0001644901,0.0001709718,0.0004311402,0.00009216266,0.00001745632,0.0001185223,0.0001872275,0.000001447424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003459263,"about_ca_system_score_gemma":0.0002429322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002600401,"about_ca_topic_score_gemma":0.00007757579,"domain_scores_codex":[0.9968458,0.00006586121,0.002178947,0.0001564678,0.0002466366,0.0005062206],"domain_scores_gemma":[0.9986199,0.00008499403,0.0005037997,0.0003037573,0.0001626824,0.0003248283],"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":[0.000143656,0.00005279537,0.03460861,0.0002977416,0.000389134,2.02999e-7,0.001604023,0.7779331,3.70965e-7,0.00007323864,0.00677284,0.1781242],"study_design_scores_gemma":[0.0009419754,0.0005724438,0.0001652255,0.00005609267,0.00009231712,0.000003477894,0.002673764,0.9791951,0.00000116039,0.00008274851,0.0160536,0.0001620914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1523695,0.001156896,0.8451067,0.0002369241,0.0003164977,0.0003303867,0.00005131384,0.000038399,0.0003934162],"genre_scores_gemma":[0.3347937,0.002209404,0.6600093,0.001723001,0.0004998141,0.00004234349,0.0006008485,0.00001688557,0.0001047359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.201262,"threshold_uncertainty_score":0.6707712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0835249501411915,"score_gpt":0.3615183872883345,"score_spread":0.277993437147143,"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."}}