{"id":"W4383652729","doi":"10.1111/epi.17710","title":"Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study","year":2023,"lang":"en","type":"article","venue":"Epilepsia","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; Libin Cardiovascular Institute of Alberta; Hotchkiss Brain Institute; University of Calgary","funders":"Epilepsy Canada; Hotchkiss Brain Institute, University of Calgary","keywords":"Interquartile range; Epilepsy; Magnetic resonance imaging; Depression (economics); Receiver operating characteristic; Electroencephalography; Medicine; Feature selection; Artificial intelligence; Machine learning; Psychology; Internal medicine; Psychiatry; Radiology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004070855,0.0005510227,0.00050339,0.0006642713,0.0001916289,0.0004542672,0.00042786,0.0003954968,0.0008622224],"category_scores_gemma":[0.009122008,0.0001906762,0.0006004025,0.0003093173,0.0002574746,0.0006371302,0.0005246961,0.0004589258,0.0002367461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004037245,"about_ca_system_score_gemma":0.0004319723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001660301,"about_ca_topic_score_gemma":0.002243104,"domain_scores_codex":[0.9990368,0.0006170829,0.00006369878,0.0001245571,0.0001022588,0.00005560412],"domain_scores_gemma":[0.9964998,0.002329591,0.0003976364,0.0002446063,0.0003523756,0.0001761082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002625181,0.001327793,0.9146676,0.0000684163,0.0003330872,0.0001503092,0.0001356503,0.00781531,0.001135295,0.00005685466,0.0002480796,0.07143638],"study_design_scores_gemma":[0.0005832062,0.01409692,0.7328131,0.00005787074,0.0003238464,0.0007769664,0.0002693047,0.2487551,0.001381005,0.0004113456,0.0004939886,0.00003719202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969162,0.0001910937,0.002346858,0.00009645132,0.000004531678,0.00008703494,0.0001124072,0.00002081753,0.0002246353],"genre_scores_gemma":[0.9975164,0.00005866825,0.002165363,0.00002547897,0.000009519524,0.00003484419,0.0001418495,0.000001793361,0.0000461068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004070855,"threshold_uncertainty_score":0.02152896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04899123874084388,"score_gpt":0.3231605482250917,"score_spread":0.2741693094842478,"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."}}