{"id":"W4401835217","doi":"10.3233/shti240519","title":"Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records","year":2024,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University; University of Toronto","funders":"","keywords":"Prediabetes; Glycated hemoglobin; Counterfactual thinking; Medicine; Artificial intelligence; Medical record; Computer science; Type 2 diabetes; Machine learning; Diabetes mellitus; Psychology; Internal medicine; Endocrinology; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001755675,0.0001799144,0.0003680498,0.0006621869,0.0003369059,0.00005349796,0.001070336,0.0001739765,0.000004400113],"category_scores_gemma":[0.00127732,0.0001564483,0.00001411441,0.001045976,0.0001943979,0.001124865,0.001729457,0.001479982,0.00001259629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002957035,"about_ca_system_score_gemma":0.0005847428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001212787,"about_ca_topic_score_gemma":0.00009852074,"domain_scores_codex":[0.9977772,0.00009379228,0.0007860906,0.000337271,0.0003011983,0.0007044764],"domain_scores_gemma":[0.998266,0.0006044264,0.0001258009,0.0008754348,0.0000447583,0.00008353031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001111757,0.00004605759,0.01488899,0.004484942,0.0001196935,0.00005781669,0.02124676,0.0003396298,8.242278e-7,0.3734236,0.001898506,0.5834821],"study_design_scores_gemma":[0.0001997304,0.0004829826,0.0002452219,0.00205986,0.00000371275,0.00003004666,0.004111983,0.932046,0.0000202707,0.03687322,0.02372611,0.0002008893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8208535,0.06878055,0.05367453,0.04870157,0.004379368,0.000904239,0.00004964329,0.0022452,0.0004113752],"genre_scores_gemma":[0.9440242,0.02705188,0.02617557,0.002461536,0.0001376031,0.0001014493,0.00001990759,0.00001835666,0.000009501912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9317063,"threshold_uncertainty_score":0.6429867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2437497425269156,"score_gpt":0.4086602700647345,"score_spread":0.1649105275378188,"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."}}