{"id":"W4389894415","doi":"10.3390/metabo13121204","title":"Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics","year":2023,"lang":"en","type":"article","venue":"Metabolites","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Diabetic retinopathy; Artificial intelligence; Feature selection; Hyperparameter; Gradient boosting; Machine learning; Naive Bayes classifier; Receiver operating characteristic; Computer science; Support vector machine; Bayesian network; Boosting (machine learning); Cross-validation; Medicine; Random forest; Diabetes mellitus; Endocrinology","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":[],"consensus_categories":[],"category_scores_codex":[0.001268225,0.0001139821,0.0003122433,0.0001970134,0.00006117993,0.00004949344,0.0001229153,0.00004163591,0.00000642525],"category_scores_gemma":[0.00384042,0.00006050311,0.00007169629,0.001094432,0.000200788,0.00006814754,0.00007417904,0.0001227931,0.000004531656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001142465,"about_ca_system_score_gemma":0.0000266153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008118767,"about_ca_topic_score_gemma":0.00002785065,"domain_scores_codex":[0.9988814,0.000109505,0.0003692679,0.0001995685,0.0001880278,0.0002522044],"domain_scores_gemma":[0.9962968,0.003226565,0.00007754079,0.0002801338,0.00008794246,0.00003100652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004539715,0.0003122309,0.8212665,0.0004583386,0.0001290041,0.00003198008,0.003275557,0.0002668895,0.03519405,0.005825491,0.0004573349,0.1323287],"study_design_scores_gemma":[0.0003552783,0.0001900516,0.8231667,0.0005126526,0.0004709695,0.000003381059,0.00515715,0.04878879,0.09479415,0.02408985,0.002252027,0.0002190203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987882,0.008965833,0.0004497399,0.002138888,0.0001396726,0.0003571737,0.00001350015,0.00001261685,0.00004058731],"genre_scores_gemma":[0.9936468,0.00572025,0.0001758348,0.00005786853,0.00005390634,0.00004893277,0.00001519278,0.00001439189,0.0002668496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1321097,"threshold_uncertainty_score":0.4597619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04381594089422114,"score_gpt":0.3141966234270187,"score_spread":0.2703806825327975,"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."}}