{"id":"W3123859422","doi":"10.18280/ria.340609","title":"Prediction of Brain Stroke Severity Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stroke (engine); Subarachnoid hemorrhage; Intracerebral hemorrhage; Medicine; Random forest; Machine learning; Predictive modelling; Ischemic stroke; Artificial intelligence; Computer science; Physical medicine and rehabilitation; Medical emergency; Cardiology; Internal medicine; Ischemia; Engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00104179,0.0002439558,0.0004833422,0.0001218326,0.0008244589,0.00001126423,0.0003787694,0.0002967628,0.001956269],"category_scores_gemma":[0.002333413,0.0002564209,0.0001589832,0.000698184,0.0001720171,0.0002202217,0.0002180342,0.001345271,0.0007390719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001680669,"about_ca_system_score_gemma":0.0002500539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001910149,"about_ca_topic_score_gemma":0.0002474319,"domain_scores_codex":[0.996173,0.0007298023,0.001538018,0.0005505322,0.0003424927,0.0006661592],"domain_scores_gemma":[0.9972629,0.0009938355,0.0005724258,0.0004058586,0.0004415214,0.0003234902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004655289,0.0002412857,0.6153012,0.002533496,0.00008714838,0.00003184755,0.05637404,0.1297483,0.1626968,0.005061985,0.001426407,0.02603204],"study_design_scores_gemma":[0.00004053966,0.0002528361,0.0002692331,0.000274493,0.0000216095,0.000003714238,0.01254405,0.9229257,0.05026086,0.0003834001,0.01283801,0.0001855157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8668112,0.0005996085,0.1192713,0.006730345,0.001104134,0.001408949,0.0002507367,0.0003747082,0.003449025],"genre_scores_gemma":[0.9956502,0.0001056011,0.001451126,0.0009970915,0.0005215561,0.00003110515,0.00003449264,0.00005164735,0.001157176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7931775,"threshold_uncertainty_score":0.9999888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.273635845607109,"score_gpt":0.428870314001659,"score_spread":0.15523446839455,"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."}}