{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001331945,0.0005442747,0.0005157907,0.0007164617,0.0001458211,0.0007248458,0.0005528132,0.0006137339,0.001135971],"category_scores_gemma":[0.003448169,0.0002551005,0.0007048596,0.0008122768,0.0002680524,0.0008801994,0.0004070181,0.001034153,0.0003732063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005143544,"about_ca_system_score_gemma":0.0006462141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002492326,"about_ca_topic_score_gemma":0.00246031,"domain_scores_codex":[0.99967,0.0001148301,0.00002244206,0.0000919419,0.00006524658,0.00003553981],"domain_scores_gemma":[0.9992042,0.0004510925,0.0001263912,0.00009884071,0.0000959653,0.00002354121],"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":[0.0003585001,0.0001821424,0.004143028,0.0001093833,0.0001495753,0.0002126802,0.0001081214,0.5275248,0.01078462,0.01936246,0.005018276,0.4320464],"study_design_scores_gemma":[0.000004508252,0.00002323908,0.0005965425,0.000005305169,0.00000857926,0.00002439172,0.000004803006,0.9948973,0.0008586268,0.003132706,0.000437909,0.00000605574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06174567,0.0005957035,0.9351212,0.0005547404,0.0001011846,0.00004756853,0.0002852751,0.0007500231,0.0007986086],"genre_scores_gemma":[0.7528781,0.0009511181,0.2413274,0.0002462107,0.0001951845,0.0001995372,0.001119836,0.00009978579,0.002982712],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002492326,"threshold_uncertainty_score":0.007044077,"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."}}