{"id":"W4404015886","doi":"10.21203/rs.3.rs-5363467/v1","title":"Synthetic Data for Accessible Learning in Healthcare: Improving Mortality Prediction with Longitudinal Data","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cambridge Memorial Hospital; McGill University Health Centre; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Longitudinal data; Health care; Computer science; Data science; Artificial intelligence; Machine learning; Data mining; Economics; Economic growth","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":["metaresearch","metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02297584,0.0004784173,0.0008391713,0.0009345211,0.001970574,0.0002963938,0.004125731,0.001169224,0.0001783395],"category_scores_gemma":[0.009443365,0.0004162443,0.00006910152,0.00119401,0.0003239839,0.0006963085,0.02207219,0.01507934,0.0002110974],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001757952,"about_ca_system_score_gemma":0.01076231,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1192228,"about_ca_topic_score_gemma":0.1395322,"domain_scores_codex":[0.9863916,0.003921772,0.001750638,0.003601116,0.00200778,0.002327112],"domain_scores_gemma":[0.9856791,0.004155965,0.0004657348,0.00739417,0.001806178,0.0004988684],"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":[0.0005966156,0.0001738007,0.8785406,0.07966051,0.0001229206,0.0001344746,0.0020522,0.0008459863,0.00002099604,0.001562969,0.007986899,0.02830203],"study_design_scores_gemma":[0.0004113799,0.0008429791,0.06310809,0.03367164,0.0001390367,0.000006925358,0.01364743,0.8539681,0.00003637578,0.02526665,0.00807861,0.0008228055],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.782806,0.02179088,0.01227298,0.06582282,0.00915381,0.05181472,0.05136609,0.002461994,0.002510667],"genre_scores_gemma":[0.9816045,0.0008584503,0.001079233,0.0000602947,0.001896396,0.00301478,0.01062607,0.0002043792,0.0006558576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8531221,"threshold_uncertainty_score":0.9998289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.653082182088013,"score_gpt":0.6384292035500618,"score_spread":0.01465297853795122,"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."}}