{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00109015,0.0002943407,0.0003851953,0.0003457921,0.0002943407,0.0001471622,0.0007921818,0.00003785809,0.00001143415],"category_scores_gemma":[0.0006139213,0.0002407232,0.00002545898,0.001002762,0.0002578695,0.0003940729,0.0008959913,0.0007593841,0.000006043704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000291191,"about_ca_system_score_gemma":0.00006218407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00121423,"about_ca_topic_score_gemma":0.001475144,"domain_scores_codex":[0.996136,0.0007846748,0.0005972561,0.00140202,0.0004540483,0.0006260213],"domain_scores_gemma":[0.9982254,0.0006728023,0.0001994884,0.0007165437,0.00003312265,0.0001526333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005102422,0.0003535567,0.9785234,0.00002008382,0.000003280946,0.0004001214,0.0007172555,0.0003274537,0.004125008,0.000003774195,0.00005904404,0.01495674],"study_design_scores_gemma":[0.001884965,0.001671048,0.6629255,0.0002918179,0.00001296305,0.00005806172,0.0002067721,0.3324811,0.0001675273,0.0000252483,0.00007894027,0.0001959509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976474,0.0007676037,0.0001806553,0.0001564847,0.0001808933,0.0007410945,0.00004153558,0.000249045,0.0000353036],"genre_scores_gemma":[0.9985878,0.0004143055,0.000592933,0.0001877315,0.00009154528,0.00003039549,0.00002201219,0.00004519511,0.0000280659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3321537,"threshold_uncertainty_score":0.9816407,"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."}}