{"id":"W4385956640","doi":"10.1145/3608251.3608292","title":"Exploring the Efficacy of Explainable Deep Learning in Identifying Neuromarkers for Precise Prediction of Epilepsy and Causal Connectivity Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Ictal; Pattern recognition (psychology); Autoregressive model; False discovery rate; Feature extraction; Preprocessor; Electroencephalography; Deep learning; Convolutional neural network; Mathematics; Statistics; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001278558,0.0006965652,0.000437678,0.0005244109,0.0002249253,0.0006409446,0.0005873697,0.000663084,0.00124077],"category_scores_gemma":[0.003307772,0.0003122551,0.000497213,0.0003249556,0.0003487269,0.001167844,0.0006144169,0.0009498702,0.0001570806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004396017,"about_ca_system_score_gemma":0.000637904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004032246,"about_ca_topic_score_gemma":0.005191932,"domain_scores_codex":[0.9997664,0.00009267434,0.00001209031,0.00006635376,0.00002896523,0.0000335104],"domain_scores_gemma":[0.9989733,0.0007456252,0.0000924636,0.0000851449,0.00007446626,0.00002916736],"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":[0.0002750941,0.0001968679,0.009779301,0.0001025794,0.0002201943,0.0001869387,0.0001042742,0.8178784,0.008443996,0.008390342,0.001019127,0.153403],"study_design_scores_gemma":[0.000002698889,0.00002097567,0.0005553295,0.000003761784,0.00000847112,0.000009257629,0.000004699383,0.9962026,0.0005224501,0.002577212,0.00009009711,0.000002438031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3509494,0.001664213,0.6429869,0.001007406,0.0000725941,0.00005236801,0.0002754897,0.0007321762,0.002259362],"genre_scores_gemma":[0.9745502,0.0003006392,0.02375098,0.00006787798,0.00002070106,0.00002148489,0.0001879369,0.00002193789,0.001078236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004032246,"threshold_uncertainty_score":0.00801754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1100349499708028,"score_gpt":0.3023412430723997,"score_spread":0.1923062931015969,"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."}}