{"id":"W4400413262","doi":"10.2196/52730","title":"Using Domain Adaptation and Inductive Transfer Learning to Improve Patient Outcome Prediction in the Intensive Care Unit: Retrospective Observational Study","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Observational study; Intensive care unit; Retrospective cohort study; Medicine; Outcome (game theory); Transfer of learning; Health care; Computer science; Emergency medicine; Medical emergency; Intensive care medicine; Artificial intelligence; Surgery; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006841028,0.0001090406,0.0002109828,0.0006131969,0.000108413,0.000285591,0.0006906893,0.0001130982,0.00002355878],"category_scores_gemma":[0.004041133,0.0000712957,0.00004979509,0.0009450394,0.00008781387,0.0003916429,0.0002710877,0.003589912,0.000003257955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005025853,"about_ca_system_score_gemma":0.000455803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001162725,"about_ca_topic_score_gemma":0.0003248537,"domain_scores_codex":[0.9937483,0.002025188,0.0006375407,0.0003208705,0.002994354,0.0002737528],"domain_scores_gemma":[0.9967179,0.0009301995,0.00006030443,0.0001514363,0.001951248,0.0001889597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001210905,0.00009228992,0.4504231,0.00009754826,0.0000665135,0.0006903679,0.5047146,0.001273511,0.00009529696,0.004524783,0.00009958111,0.03780131],"study_design_scores_gemma":[0.0006943197,0.005631051,0.4246377,0.0007894043,0.00001087184,0.0002549627,0.2650928,0.3006211,0.00002411545,0.001432546,0.0006938655,0.0001171918],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9398533,0.0002527783,0.05049243,0.008298011,0.0005568998,0.0004905599,0.000002014669,0.00001405277,0.00004000487],"genre_scores_gemma":[0.9982885,0.000008233053,0.001153705,0.0002624463,0.0002350612,0.00001852396,0.000001057337,0.000009937403,0.00002258208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2993476,"threshold_uncertainty_score":0.9987088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2749397512186034,"score_gpt":0.4800483655802536,"score_spread":0.2051086143616502,"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."}}