{"id":"W4294975841","doi":"10.1109/iri54793.2022.00069","title":"Adding Explainability to Machine Learning Models to Detect Chronic Kidney Disease","year":2022,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Kidney disease; Interpretability; Renal function; Medicine; Intensive care medicine; Disease; Machine learning; Random forest; Renal replacement therapy; Stage (stratigraphy); Computer science; Internal medicine; Artificial intelligence","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001654596,0.0002116187,0.0002824617,0.0002173669,0.003964857,0.00001262575,0.0004469389,0.00006720533,0.01380114],"category_scores_gemma":[0.002142301,0.0002124109,0.00009198052,0.0006893845,0.00002267563,0.0001531709,0.001204143,0.001427847,0.001049982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002707421,"about_ca_system_score_gemma":0.001566318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008868613,"about_ca_topic_score_gemma":0.004122642,"domain_scores_codex":[0.9955553,0.001480211,0.000730305,0.0006728925,0.0005146952,0.001046618],"domain_scores_gemma":[0.9966289,0.0006709931,0.0001089058,0.0006312068,0.0001996439,0.001760284],"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":[0.00175749,0.0001757196,0.1426017,0.001398046,0.00003826529,0.00008542494,0.04793143,0.6648901,0.001265745,0.01996921,0.04165821,0.07822868],"study_design_scores_gemma":[0.000300127,0.001111047,0.001614516,0.0002810572,0.00002876722,0.000001695504,0.02603745,0.4875435,0.0003293655,0.01453562,0.4673048,0.000912075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8616399,0.0005720943,0.06062177,0.04919534,0.003168085,0.009623648,0.0005783441,0.001678892,0.01292195],"genre_scores_gemma":[0.9791444,0.000008003266,0.00131702,0.01081275,0.0003167067,0.002676184,0.00004412723,0.00005869918,0.005622134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4256466,"threshold_uncertainty_score":0.9997278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.19028713375368,"score_gpt":0.4549462510061526,"score_spread":0.2646591172524726,"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."}}