{"id":"W4388641629","doi":"10.2196/50328","title":"A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Interpretability; Mobile apps; Computer science; Machine learning; Diabetes mellitus; Artificial intelligence; Human–computer interaction; Data science; World Wide Web; 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":[0.008529716,0.0007104776,0.0008029561,0.0009827632,0.0005751878,0.00108103,0.000684368,0.001242155,0.002059803],"category_scores_gemma":[0.02933441,0.0004139993,0.0006789354,0.0004107101,0.0005485072,0.00132099,0.001291389,0.0009914312,0.00071682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004098985,"about_ca_system_score_gemma":0.000437846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001235976,"about_ca_topic_score_gemma":0.001616717,"domain_scores_codex":[0.9964973,0.002028192,0.0003004203,0.0003531154,0.0005323495,0.000288672],"domain_scores_gemma":[0.963356,0.02856115,0.001237302,0.001272757,0.004580749,0.0009920652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.004035472,0.01892982,0.5394631,0.004381539,0.0005920389,0.005284989,0.1278442,0.002824326,0.01922945,0.0009499236,0.01787744,0.2585877],"study_design_scores_gemma":[0.001236212,0.04678752,0.7093161,0.001802114,0.001127903,0.01221818,0.1027569,0.05696505,0.01820397,0.001454775,0.04736052,0.0007707858],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951068,0.0001365171,0.002993867,0.0002121471,0.00001753559,0.0003985345,0.0003173551,0.0001483897,0.0006688694],"genre_scores_gemma":[0.9899654,0.0002296853,0.007067926,0.0004684663,0.00002758896,0.0008661575,0.0004516401,0.00005407847,0.0008689237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008529716,"threshold_uncertainty_score":0.04511005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1623964573883651,"score_gpt":0.4401856112530089,"score_spread":0.2777891538646439,"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."}}