{"id":"W4413051491","doi":"10.3233/shti250954","title":"Evaluating Zero-Shot Foundation Models for Time Series Forecasting in Clinical Settings: A Simulation Study with Electronic Health Records","year":2025,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Health records; Electronic health record; Foundation (evidence); Series (stratigraphy); Zero (linguistics); Shot (pellet); Computer science; Time series; Machine learning; History; Health care; Geology; Political science; Archaeology","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":[0.006978108,0.0009032097,0.001078137,0.0006054752,0.0004774693,0.0009452772,0.001416955,0.001594372,0.0008986681],"category_scores_gemma":[0.0220948,0.0004104843,0.0008267983,0.0004554229,0.0007640123,0.001556868,0.001079637,0.002025866,0.0001837595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001394716,"about_ca_system_score_gemma":0.001490893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01815031,"about_ca_topic_score_gemma":0.01374578,"domain_scores_codex":[0.9988829,0.0006457152,0.00005419257,0.0002297988,0.00008001654,0.0001072892],"domain_scores_gemma":[0.9820964,0.01564781,0.0005622452,0.0005993225,0.0006519167,0.0004423428],"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":[0.0007522675,0.0004018224,0.0098069,0.0001207534,0.0001208163,0.0001182758,0.0001969829,0.9610608,0.0005139418,0.003921346,0.000908624,0.02207751],"study_design_scores_gemma":[0.00002995727,0.000157243,0.001250928,0.00001372376,0.00001353407,0.00001641129,0.00004377253,0.9962693,0.0002651986,0.001813187,0.000115142,0.0000115632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279011,0.0006107908,0.06835146,0.0007846322,0.00007904683,0.0001399493,0.0004687627,0.0003025826,0.00136166],"genre_scores_gemma":[0.9878812,0.0001022659,0.01100114,0.00009424044,0.0000156233,0.00006412809,0.0004293916,0.00001066051,0.0004012144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01815031,"threshold_uncertainty_score":0.03690422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1318690127842805,"score_gpt":0.4816748992979237,"score_spread":0.3498058865136432,"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."}}