{"id":"W4380190507","doi":"10.1007/978-3-031-34619-4_10","title":"PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Kidney disease; Medicine; Disease; Machine learning; Artificial intelligence; Intensive care medicine; Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006886218,0.0002113703,0.0003232531,0.0001982513,0.002167855,0.00002752179,0.00050067,0.0002182755,0.000001263687],"category_scores_gemma":[0.0004571484,0.0001711781,0.0001164394,0.0001187236,0.0002734147,0.0001336799,0.0003236211,0.0005881344,2.964007e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487963,"about_ca_system_score_gemma":0.00111966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001197459,"about_ca_topic_score_gemma":0.0004633686,"domain_scores_codex":[0.9984172,0.00002700209,0.0009630651,0.0001422763,0.0002080455,0.0002423833],"domain_scores_gemma":[0.9976811,0.0008964413,0.0007890719,0.000276338,0.000253613,0.0001034688],"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":[0.00000708699,0.000006429375,0.0003553187,0.001660142,0.00005350552,8.406728e-9,0.005720445,0.9865255,0.000009187158,0.002022685,0.0000229495,0.003616691],"study_design_scores_gemma":[0.0001183333,0.00004392907,0.0002608994,0.000749111,0.00007526692,6.251837e-8,0.00002821364,0.9951324,0.0000540321,0.0007265354,0.002663885,0.0001472997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003834531,0.0001843107,0.9923882,0.0002905641,0.0005037183,0.001640472,0.00103179,0.00007449697,0.00005194408],"genre_scores_gemma":[0.2343928,0.0004343603,0.7635527,0.00009483274,0.0002174313,0.0002910389,0.0007417591,0.00006555786,0.0002095348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2305583,"threshold_uncertainty_score":0.9991312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.092521679932356,"score_gpt":0.3543387385905006,"score_spread":0.2618170586581446,"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."}}