{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003262567,0.001276379,0.001166295,0.002227794,0.0005745112,0.0005473979,0.001204691,0.001210277,0.0007285675],"category_scores_gemma":[0.004226949,0.0002965921,0.00124107,0.0009015889,0.0003430746,0.0005893304,0.0003855946,0.0009249891,0.0004261957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004476351,"about_ca_system_score_gemma":0.0008948598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007321565,"about_ca_topic_score_gemma":0.007428429,"domain_scores_codex":[0.9988881,0.0003559597,0.00007072753,0.0003411965,0.0001836828,0.0001603577],"domain_scores_gemma":[0.996885,0.001993198,0.0002458599,0.0001319758,0.0006435786,0.0001003227],"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":[0.0006540342,0.0006197488,0.01556647,0.0001675498,0.0002911192,0.0003570266,0.00009023868,0.5390908,0.01273164,0.001128138,0.003741562,0.4255617],"study_design_scores_gemma":[0.00001131275,0.00004370521,0.001165002,0.000008054252,0.00002320573,0.00004836489,0.000007366608,0.9967578,0.001135997,0.0006259303,0.0001634089,0.000009727854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09859352,0.0006378401,0.897543,0.0001766821,0.00006797077,0.000162053,0.0002819283,0.001943529,0.0005935064],"genre_scores_gemma":[0.722068,0.0002480749,0.274872,0.0001737104,0.000152121,0.0002102449,0.00135833,0.0001214564,0.0007959741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007321565,"threshold_uncertainty_score":0.01725429,"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."}}