{"id":"W3040788463","doi":"10.1038/s41598-020-67952-0","title":"Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Southeastern Ontario Academic Medical Organization; Ontario Ministry of Research and Innovation; Queen's University","keywords":"Critically ill; Intensive care medicine; Medicine; Computer science; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00180996,0.0001776883,0.0003740297,0.000182348,0.0002389,0.000207808,0.0005013459,0.00009591562,0.000007746697],"category_scores_gemma":[0.003183305,0.0001658184,0.0001298537,0.0008248588,0.0001610916,0.000601476,0.0003014478,0.0001900144,0.000001129797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005447229,"about_ca_system_score_gemma":0.0002142244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002293197,"about_ca_topic_score_gemma":0.00001548775,"domain_scores_codex":[0.9964139,0.0001793647,0.0009325502,0.001192034,0.000795857,0.0004862603],"domain_scores_gemma":[0.9966843,0.0001131825,0.0003886875,0.0007187059,0.001860867,0.0002342466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001646338,0.001073915,0.5903669,0.002997139,0.00005694757,0.0003341208,0.0359322,0.316655,0.003525375,0.02201377,0.01065874,0.01622129],"study_design_scores_gemma":[0.0003415027,0.0001005989,0.0007535432,0.00005335675,0.000005736455,0.00002085063,0.0001632698,0.9866339,0.0002504972,0.01078401,0.0007236631,0.0001690869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2135355,0.0002039404,0.7813841,0.001881623,0.001339391,0.0009897876,0.000005241641,0.0001563059,0.0005041711],"genre_scores_gemma":[0.7311038,0.000001012517,0.2680936,0.0005326055,0.00004355465,0.0000542223,0.00005733138,0.00001658589,0.00009725546],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6699789,"threshold_uncertainty_score":0.6761878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03770574000853056,"score_gpt":0.2729263830654099,"score_spread":0.2352206430568794,"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."}}