{"id":"W4400487604","doi":"10.2196/54044","title":"Predictive Model for Extended-Spectrum β-Lactamase–Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Japan Science and Technology Agency","keywords":"Receiver operating characteristic; Logistic regression; Artificial intelligence; Machine learning; Intensive care unit; Medical record; Computer science; Set (abstract data type); Data set; Demographics; Medicine; Intensive care medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003077234,0.0006041255,0.0006518955,0.001811069,0.0004403312,0.000968972,0.000798305,0.0008223468,0.001471496],"category_scores_gemma":[0.01103317,0.0005479744,0.001346868,0.001253576,0.000383687,0.0009633417,0.0006259492,0.001637391,0.0004249822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006272385,"about_ca_system_score_gemma":0.000808216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005254124,"about_ca_topic_score_gemma":0.002935006,"domain_scores_codex":[0.9984817,0.0004672898,0.0001928515,0.0004285991,0.0002898784,0.0001397541],"domain_scores_gemma":[0.9911986,0.004331175,0.0021734,0.0007825225,0.001051726,0.0004625245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001996036,0.0002089816,0.9957598,0.00002863254,0.0000967284,0.0003999859,0.0001307937,0.0007213634,0.0001110255,0.00004466847,0.0003186646,0.00197965],"study_design_scores_gemma":[0.00004640526,0.0009830086,0.9428797,0.00006484235,0.0002721065,0.002458562,0.001683209,0.04966493,0.0004976837,0.0003418818,0.001059527,0.00004817498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968675,0.0001641562,0.001647631,0.00005007551,0.00000847043,0.00003697802,0.001071517,0.00001173999,0.0001418327],"genre_scores_gemma":[0.9968742,0.0001426217,0.0009554702,0.00002506329,0.00001710082,0.0000564378,0.001807906,0.000006746419,0.0001143038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005254124,"threshold_uncertainty_score":0.01627421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1858920037287402,"score_gpt":0.4828916336034931,"score_spread":0.2969996298747529,"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."}}