{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00213295,0.0008479531,0.000962966,0.00131297,0.0002385474,0.001242648,0.0005990483,0.0006499196,0.001937169],"category_scores_gemma":[0.0122524,0.0003407302,0.0008389925,0.0007859205,0.0001878137,0.0007749699,0.0007098899,0.0007209211,0.0006316951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000765677,"about_ca_system_score_gemma":0.001011613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008977773,"about_ca_topic_score_gemma":0.007016787,"domain_scores_codex":[0.9990752,0.000391815,0.00009331694,0.0001905035,0.0001336357,0.0001154431],"domain_scores_gemma":[0.9953205,0.002732682,0.0008938861,0.0002821862,0.0005967196,0.0001740439],"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":[0.001007004,0.0005617151,0.5298259,0.0002165204,0.0006368014,0.0003732456,0.0001125504,0.372016,0.001109433,0.001008976,0.004536404,0.08859546],"study_design_scores_gemma":[0.00004594357,0.0002041809,0.0570144,0.00005160675,0.00008384819,0.0001712347,0.00004423743,0.9397183,0.0002962491,0.00162088,0.0007308123,0.00001834135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9134589,0.001294642,0.07881051,0.0009781909,0.0001149499,0.0001317794,0.002421582,0.0006325922,0.002156794],"genre_scores_gemma":[0.9863542,0.0002359659,0.01107463,0.0001261653,0.00003986956,0.00004837043,0.001712995,0.0000174634,0.0003903554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008977773,"threshold_uncertainty_score":0.01785105,"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."}}