{"id":"W3048678791","doi":"10.2196/18331","title":"Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Gout, Hyperuricemia, Uric Acid","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Machine learning; Decision tree; Medicine; Artificial intelligence; Uric acid; Population; Computer science; Linear regression; Environmental health; 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":[0.009996425,0.001864067,0.002059143,0.002170309,0.000582197,0.001543625,0.001906577,0.00192892,0.002261542],"category_scores_gemma":[0.01749956,0.0004911124,0.001807961,0.001779072,0.0004143371,0.001666126,0.001232929,0.002288951,0.0006538947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002120499,"about_ca_system_score_gemma":0.002306117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02499947,"about_ca_topic_score_gemma":0.006784201,"domain_scores_codex":[0.9971455,0.001591961,0.0002584021,0.0004314643,0.0003569995,0.000215718],"domain_scores_gemma":[0.9845123,0.01223368,0.0005270669,0.0004923556,0.002011233,0.0002233797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008859143,0.0006839423,0.01457569,0.0003289092,0.0004070166,0.00008795588,0.00005548578,0.8780395,0.0004048055,0.0009298293,0.002354688,0.1012462],"study_design_scores_gemma":[0.00002173343,0.0001069036,0.0008157392,0.00001411459,0.00002557046,0.00001097278,0.000008754342,0.9982517,0.000232071,0.0003776772,0.0001258716,0.000008838145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5799654,0.01503009,0.3864779,0.002681059,0.0006087312,0.001065098,0.002837515,0.004960825,0.006373385],"genre_scores_gemma":[0.872668,0.002119602,0.1198505,0.0002352602,0.0001612265,0.0008010667,0.002684737,0.0001293912,0.001350072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02499947,"threshold_uncertainty_score":0.05286682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246432107523374,"score_gpt":0.2540455790390092,"score_spread":0.2315812579637754,"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."}}