{"id":"W4281932345","doi":"10.14309/ctg.0000000000000507","title":"Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease","year":2022,"lang":"en","type":"article","venue":"Clinical and Translational Gastroenterology","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"U.S. National Library of Medicine; National Human Genome Research Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Medicine; Logistic regression; Inflammatory bowel disease; Receiver operating characteristic; Decision tree; Retrospective cohort study; Health care; Decile; Healthcare Cost and Utilization Project; Comorbidity; Emergency medicine; Internal medicine; Machine learning; Disease; Statistics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002709759,0.0002285316,0.0002147988,0.00005936258,0.0002966929,0.00002265285,0.0001958606,0.00008456632,0.00009489039],"category_scores_gemma":[0.00003880016,0.0002165702,0.0001178755,0.0000368703,0.0002765073,0.00001607946,0.000149825,0.0003717731,0.000003121521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007898672,"about_ca_system_score_gemma":0.0001008791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001014113,"about_ca_topic_score_gemma":0.0001282694,"domain_scores_codex":[0.9978908,0.0004401026,0.0005078775,0.0005576872,0.000261167,0.0003423773],"domain_scores_gemma":[0.9991345,0.00004146163,0.0001674816,0.0002244429,0.0001033134,0.0003288143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00938363,0.0002565102,0.9652289,0.00002084391,0.000129024,0.00005751616,0.00001821129,0.02417323,0.00006652094,0.0003314356,0.0000786798,0.0002555111],"study_design_scores_gemma":[0.005916026,0.0006168481,0.9900153,0.000004415574,0.00008148926,0.000004440155,0.000008667376,0.001802465,0.000003213086,0.000693919,0.000689363,0.000163884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968786,0.0000374322,0.001030438,0.0007259574,0.0001944758,0.0003874872,0.000708054,0.00002580003,0.00001175568],"genre_scores_gemma":[0.9949099,0.00001496876,0.0001273459,0.001276343,0.0001336346,0.0001196994,0.003318588,0.00003477078,0.00006475038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02478638,"threshold_uncertainty_score":0.8831478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182410394727715,"score_gpt":0.2671311296337393,"score_spread":0.2453070256864621,"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."}}