{"id":"W4388153871","doi":"10.1016/j.eswa.2023.122402","title":"SRTNet: Scanning, Reading, and Thinking Network for myocardial infarction detection and localization","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Athabasca University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Overfitting; Computer science; Artificial intelligence; Pattern recognition (psychology); Deep learning; Sensitivity (control systems); Perspective (graphical); Feature (linguistics); Machine learning; Artificial neural network","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":[0.0002311835,0.00009233794,0.000164815,0.000114244,0.0004403985,0.00005653078,0.00002256129,0.00007024617,6.616135e-7],"category_scores_gemma":[0.000014335,0.00007840755,0.00002520901,0.0004545205,0.0000343857,0.00005121415,0.00001235523,0.00005465394,0.000003850191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003957401,"about_ca_system_score_gemma":0.00001592399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003899583,"about_ca_topic_score_gemma":0.00001368058,"domain_scores_codex":[0.9992972,0.00001985941,0.0001573572,0.0002448016,0.0001287593,0.0001520153],"domain_scores_gemma":[0.9995347,0.00005395417,0.00008024683,0.0001624835,0.0000875898,0.00008103865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005937946,0.0001968045,0.7064318,0.00152723,0.002279139,0.00000892665,0.02238638,0.0304829,0.01697241,0.01222381,0.0308249,0.1760719],"study_design_scores_gemma":[0.002804033,0.0006398273,0.07253505,0.001091058,0.00067542,0.0002236784,0.006269415,0.3614531,0.0006179491,0.0005445282,0.5524728,0.0006731139],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04394211,0.002453617,0.9496608,0.0004143282,0.0003096173,0.001984668,0.000006168781,0.000581809,0.0006468216],"genre_scores_gemma":[0.9939533,0.0001954341,0.001212655,0.00005922576,0.00154055,0.00214028,0.0000658171,0.0000276858,0.0008050928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9500111,"threshold_uncertainty_score":0.3387234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389833318439415,"score_gpt":0.2817648014158918,"score_spread":0.2678664682314977,"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."}}