{"id":"W2991182470","doi":"10.1109/chase48038.2019.00008","title":"Poster Abstract: A Machine Learning Approach to Identify High-Cost Elderly Renal Transplant Recipients","year":2019,"lang":"en","type":"article","venue":"","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Renal transplant; Logistic regression; Lasso (programming language); Medicine; Health care; Computer science; Random forest; End stage renal disease; Artificial intelligence; Machine learning; Disease; Intensive care medicine; Transplantation; Internal medicine; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001826715,0.0004318406,0.0004930962,0.001079161,0.00109348,0.001484886,0.0005460851,0.0005749332,0.005354581],"category_scores_gemma":[0.005854486,0.0001184048,0.000626076,0.0007347083,0.0002336323,0.0004524183,0.0005538719,0.0007571858,0.0008520433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002410756,"about_ca_system_score_gemma":0.004363211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1587547,"about_ca_topic_score_gemma":0.2110501,"domain_scores_codex":[0.9994953,0.0001649074,0.00003048532,0.0001145654,0.0001075963,0.00008711684],"domain_scores_gemma":[0.9984148,0.0005471366,0.0001412121,0.00006160529,0.0006810317,0.0001542816],"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":[0.0008459674,0.0008057319,0.5636539,0.0002848686,0.0003370611,0.0003542577,0.0005080178,0.0522826,0.001812662,0.003046912,0.04464813,0.3314199],"study_design_scores_gemma":[0.0001695276,0.0003472846,0.2649724,0.0002019748,0.0002135089,0.0002660858,0.001374413,0.7127576,0.00200428,0.006291258,0.01132456,0.00007695252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.814933,0.001941499,0.1245084,0.01532586,0.000712361,0.001332217,0.01050872,0.0008606553,0.02987737],"genre_scores_gemma":[0.9467469,0.0002801327,0.04036295,0.0007996343,0.0001955252,0.0002013449,0.002219038,0.00002992294,0.009164535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1587547,"threshold_uncertainty_score":0.3156613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02559743661683248,"score_gpt":0.3005365439290426,"score_spread":0.2749391073122101,"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."}}