{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001101264,0.0006286372,0.0006282158,0.002598564,0.0002076612,0.000797186,0.00040743,0.0005447444,0.001047892],"category_scores_gemma":[0.004443458,0.0001490077,0.0006921481,0.001298762,0.0001701081,0.0007026262,0.0002931111,0.0007550544,0.0005132043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003504692,"about_ca_system_score_gemma":0.0005847879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005864849,"about_ca_topic_score_gemma":0.004713586,"domain_scores_codex":[0.9994158,0.0001450374,0.00007208474,0.0001247605,0.0001754634,0.00006693065],"domain_scores_gemma":[0.9984894,0.0008729104,0.0001715934,0.00007719421,0.0003284008,0.0000605014],"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":[0.0003598094,0.0005414223,0.2189812,0.0002094798,0.0003223975,0.0002846623,0.00008260847,0.2556832,0.001937111,0.001147517,0.006910502,0.5135401],"study_design_scores_gemma":[0.00001171614,0.0001543158,0.02327966,0.00003399308,0.00004836157,0.0001138759,0.00003715005,0.9716884,0.001073807,0.002447589,0.001089321,0.00002184317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6526876,0.00542772,0.325682,0.001944109,0.000532768,0.0003386707,0.004122499,0.002376973,0.006887747],"genre_scores_gemma":[0.9699798,0.00107407,0.02495947,0.0001090455,0.0001678325,0.00007378821,0.002207011,0.00001731984,0.001411779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005864849,"threshold_uncertainty_score":0.01166147,"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."}}