{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007484084,0.0008786065,0.0006175681,0.00171715,0.0003401773,0.0006740283,0.00107331,0.0006635405,0.01832179],"category_scores_gemma":[0.002507687,0.000250255,0.000375837,0.0007262836,0.000165501,0.0007442027,0.00112889,0.0006507235,0.01038007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004380888,"about_ca_system_score_gemma":0.0009944973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004023928,"about_ca_topic_score_gemma":0.007645219,"domain_scores_codex":[0.9996756,0.00006926749,0.0000238204,0.00008522817,0.00009807832,0.00004798755],"domain_scores_gemma":[0.9991092,0.0002947375,0.00007995441,0.0001417163,0.0002161045,0.000158346],"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":[0.002264963,0.0004735827,0.01984055,0.0004911839,0.0001714778,0.0004807643,0.0001742763,0.007547376,0.01367571,0.00252183,0.3978986,0.5544596],"study_design_scores_gemma":[0.00117092,0.00170993,0.04650181,0.0003682751,0.000705728,0.002791995,0.0003852129,0.5821627,0.06027375,0.01774057,0.2858851,0.0003040286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1167031,0.002775311,0.398369,0.002990659,0.00137722,0.002398426,0.1184123,0.3106239,0.04635008],"genre_scores_gemma":[0.4256118,0.001734917,0.3679628,0.002477281,0.0006544881,0.003012996,0.1364135,0.005570908,0.05656131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01832179,"threshold_uncertainty_score":0.06129247,"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."}}