{"id":"W4293420779","doi":"10.3390/a15090308","title":"Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning Models","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Lakehead University","funders":"","keywords":"Kidney disease; Computer science; Survivability; Artificial intelligence; Artificial neural network; Predictive modelling; Machine learning; Binary classification; Deep learning; F1 score; Medicine; Internal medicine; Support vector machine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000411559,0.0001264241,0.0004261274,0.0003899064,0.0009083669,0.000002215919,0.0002347309,0.00007437913,0.001166865],"category_scores_gemma":[0.0000950998,0.0001342672,0.0001569669,0.00133754,0.00009530743,0.0001654655,0.0002313331,0.0007737982,0.00001692334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003817583,"about_ca_system_score_gemma":0.0004718201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002133713,"about_ca_topic_score_gemma":0.00006143127,"domain_scores_codex":[0.9973103,0.0006362861,0.0008299805,0.0002904605,0.0005437654,0.0003892407],"domain_scores_gemma":[0.9981099,0.0003240326,0.0005157689,0.0003629497,0.0004657197,0.0002216137],"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":[0.0001746096,0.0001361228,0.3876851,0.0006912847,0.000512947,0.000005781373,0.01455629,0.5655609,0.0002580027,0.000487812,0.0001409742,0.02979027],"study_design_scores_gemma":[0.0001241464,0.0003157155,0.07331122,0.00006976946,0.0003140724,2.791901e-7,0.003877823,0.9207004,0.00004217179,0.0001850071,0.0009747705,0.00008466563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988924,0.001059535,0.007667981,0.0001972586,0.0006120446,0.0006816203,0.0004475455,0.00008251276,0.000327496],"genre_scores_gemma":[0.998494,0.0002276948,0.0002411738,0.0001237003,0.0001261637,0.0002530583,0.0002207107,0.00002323295,0.0002902535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3551395,"threshold_uncertainty_score":0.9997462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1056903421618994,"score_gpt":0.3895465118937039,"score_spread":0.2838561697318046,"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."}}