{"id":"W3089651655","doi":"10.1097/mpg.0000000000002956","title":"Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier","year":2020,"lang":"en","type":"article","venue":"Journal of Pediatric Gastroenterology and Nutrition","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome Canada; SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Medicine; Random forest; Classifier (UML); Ulcerative colitis; Artificial intelligence; Inflammatory bowel disease; Cluster analysis; Pattern recognition (psychology); Gastroenterology; Internal medicine; Disease; 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":[0.002284823,0.0006185613,0.000885975,0.001333635,0.0003601917,0.0005265197,0.0006758081,0.0008079684,0.0006905763],"category_scores_gemma":[0.003574146,0.0001422262,0.0008059304,0.0005481291,0.0001586878,0.0005042179,0.0002924647,0.0006677923,0.0006739663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005550816,"about_ca_system_score_gemma":0.000826651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005122509,"about_ca_topic_score_gemma":0.004427457,"domain_scores_codex":[0.9990476,0.0002016977,0.0001112672,0.0002754231,0.0002116266,0.0001524599],"domain_scores_gemma":[0.9979095,0.0007953804,0.0002489833,0.0002023158,0.0007395044,0.0001043093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001278433,0.0006593872,0.3507847,0.0001372759,0.0002839789,0.00048506,0.0001406318,0.07696114,0.02105257,0.0003526296,0.007549386,0.5403148],"study_design_scores_gemma":[0.00008562637,0.000557319,0.06611782,0.00005940109,0.0001547479,0.0009732166,0.00008523546,0.9199661,0.009799012,0.0007304219,0.001428945,0.00004213719],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8443485,0.001006779,0.1497621,0.0003510537,0.0001248271,0.0002030114,0.001515109,0.001404768,0.001283901],"genre_scores_gemma":[0.923852,0.0001370617,0.07266824,0.00009615155,0.00006364921,0.00008864664,0.002627973,0.00003436441,0.0004318923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005122509,"threshold_uncertainty_score":0.01208347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01579094846378605,"score_gpt":0.2409416515779955,"score_spread":0.2251507031142094,"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."}}