{"id":"W3005694506","doi":"10.2196/17061","title":"Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Neurological Disorders and Stroke; National Institutes of Health","keywords":"Electroencephalography; Random forest; Artifact (error); Computer science; Artificial intelligence; Epilepsy; Pattern recognition (psychology); Speech recognition; Machine learning; Psychology; Neuroscience","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":[0.0001548462,0.0001472609,0.0002833305,0.0000551538,0.00007107703,0.00004574668,0.0004632628,0.0001403924,0.00007111137],"category_scores_gemma":[0.0006083908,0.0001052716,0.0001014576,0.000300701,0.0002032019,0.0002679663,0.0001440632,0.0003496008,0.00001746685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001222433,"about_ca_system_score_gemma":0.00005633218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002886916,"about_ca_topic_score_gemma":7.660565e-7,"domain_scores_codex":[0.9981145,0.00008869896,0.0006114386,0.0001345055,0.0007668803,0.0002839263],"domain_scores_gemma":[0.9990897,0.0002161216,0.0001962404,0.0001522805,0.00003961192,0.0003060707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00303852,0.001335497,0.001033473,0.002621225,0.0001213949,0.00008886696,0.05851346,0.00188003,0.7788469,0.004110783,0.01940919,0.1290007],"study_design_scores_gemma":[0.002848124,0.0005815143,0.000100183,0.00005292922,0.00001304545,0.0001131546,0.0003690807,0.7266738,0.2646352,0.00009987679,0.004330046,0.000183019],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8873433,0.00002409341,0.1094704,0.0004963074,0.0002130116,0.0004019812,0.000007796344,0.0001457288,0.001897403],"genre_scores_gemma":[0.9945826,0.00002440888,0.002523288,0.002641858,0.0001625679,0.0000315181,0.000008933217,0.000009197932,0.00001564312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7247938,"threshold_uncertainty_score":0.4292853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215792572373441,"score_gpt":0.2758977838969112,"score_spread":0.2537398581731767,"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."}}