{"id":"W4372346309","doi":"10.1109/icassp49357.2023.10094305","title":"Representation Learning of Clinical Multivariate Time Series with Random Filter Banks","year":2023,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Concatenation (mathematics); Computer science; Series (stratigraphy); Time series; Artificial intelligence; Machine learning; Random forest; Classifier (UML); Multivariate statistics; Representation (politics); Time domain; Generalization; Pattern recognition (psychology); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007322039,0.00007414056,0.0002758144,0.00007238847,0.00008395128,0.00006595135,0.0002184945,0.00003635187,0.0001907848],"category_scores_gemma":[0.0003453123,0.00005044455,0.000104787,0.000701403,0.00004795636,0.0004554397,0.000168865,0.0001014528,0.0001442513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002605376,"about_ca_system_score_gemma":0.0000200118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007721494,"about_ca_topic_score_gemma":0.000006345394,"domain_scores_codex":[0.9988732,0.0001322986,0.0003908299,0.0002640495,0.000191153,0.0001485042],"domain_scores_gemma":[0.999069,0.0002984895,0.0001837141,0.000257281,0.0001499432,0.00004152605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001635866,0.0002351766,0.1740696,0.00009050815,0.001104389,0.0001289959,0.006328347,0.1226334,0.006795673,0.02605363,0.01158585,0.6493385],"study_design_scores_gemma":[0.001283593,0.0002404628,0.04680701,0.00002366526,0.00002109285,0.000003653534,0.0001405513,0.9472774,0.001855319,0.0002057926,0.002001511,0.0001399842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2436718,0.00001515909,0.7275152,0.0009453291,0.0002531435,0.0002292685,0.000001743236,0.0006828996,0.02668555],"genre_scores_gemma":[0.8904738,0.00001155135,0.07620478,0.00004682941,0.0001296949,0.000007751145,0.00001745637,0.00001359942,0.03309448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.824644,"threshold_uncertainty_score":0.2088959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04180186707888728,"score_gpt":0.3151818964222889,"score_spread":0.2733800293434017,"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."}}