{"id":"W4405427318","doi":"10.1145/3698587.3701364","title":"TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare","year":2024,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"","keywords":"Computer science; Time series; Transformer; Term (time); Series (stratigraphy); Health care; Machine learning; Engineering; Electrical engineering; Voltage; Economics; Geology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002235182,0.0002455362,0.0002536844,0.0002070493,0.00007031885,0.0001317578,0.0001349648,0.0001166052,0.0001043944],"category_scores_gemma":[0.00002246546,0.0002457337,0.00009964588,0.0003370372,0.00002195957,0.0007605099,0.00001289236,0.0002344338,0.00004394581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001594394,"about_ca_system_score_gemma":0.00005062706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003868276,"about_ca_topic_score_gemma":0.0001809908,"domain_scores_codex":[0.9985771,0.00001086101,0.0003465117,0.0002907576,0.0001389625,0.0006357943],"domain_scores_gemma":[0.9995348,0.0001765021,0.00001157822,0.0001428587,0.00002566717,0.0001085399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001734836,0.00005635882,0.05737163,0.01167083,0.0003696484,0.0002977708,0.01047318,0.007939194,0.5612082,0.003697381,0.001182796,0.3455595],"study_design_scores_gemma":[0.002883515,0.001071152,0.01652748,0.008207568,0.0001725134,0.0004384564,0.0008812051,0.1647071,0.7874011,0.00756532,0.006021999,0.00412261],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7160566,0.01111427,0.2452707,0.0007310517,0.00305831,0.00278849,0.0001682799,0.003683805,0.01712844],"genre_scores_gemma":[0.9912049,0.00004781818,0.006878615,0.0000158097,0.000362197,0.0001803458,0.00003396526,0.0001215154,0.001154878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3414369,"threshold_uncertainty_score":0.9999995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772904476951612,"score_gpt":0.2726413892400313,"score_spread":0.2349123444705152,"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."}}