{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009637233,0.0007789596,0.0007878221,0.001585089,0.0004532915,0.001723932,0.001270009,0.001552072,0.004052806],"category_scores_gemma":[0.06344458,0.0005041353,0.001034665,0.001558457,0.0006939986,0.002246752,0.001859302,0.002401028,0.0009608791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005480507,"about_ca_system_score_gemma":0.001072665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002865298,"about_ca_topic_score_gemma":0.002475691,"domain_scores_codex":[0.9962838,0.002661894,0.0001609742,0.0003702653,0.0004284772,0.00009465966],"domain_scores_gemma":[0.963051,0.02768848,0.0009948582,0.005330603,0.002261065,0.0006738491],"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":[0.002472258,0.001621805,0.07465917,0.0006892125,0.0007571487,0.0003079091,0.0003997384,0.5017554,0.002799182,0.0216453,0.06482463,0.3280684],"study_design_scores_gemma":[0.00008259394,0.000134227,0.003202423,0.00005179082,0.00003473104,0.00004800157,0.00007192549,0.96985,0.0009665314,0.02256538,0.002972014,0.00002036808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3126843,0.002663117,0.6450081,0.009125113,0.001617258,0.0002342439,0.02236646,0.003115669,0.003185776],"genre_scores_gemma":[0.8639667,0.0008139029,0.1017568,0.000491098,0.0005144268,0.0002788445,0.03022594,0.0002140306,0.001738203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009637233,"threshold_uncertainty_score":0.05096722,"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."}}