{"id":"W4392857356","doi":"10.1016/j.cmpb.2024.108122","title":"A novel feature-level fusion scheme with multimodal attention CNN for heart sound classification","year":2024,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Feature (linguistics); Artificial intelligence; Focus (optics); Pattern recognition (psychology); Feature extraction; Exploit; Domain (mathematical analysis); Mel-frequency cepstrum; Machine learning; Speech recognition","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001099983,0.0001809845,0.0003390436,0.0004180324,0.0000676711,0.00006414692,0.00005063384,0.0001427114,0.000001837783],"category_scores_gemma":[0.00002017425,0.0001210931,0.0001064479,0.000776297,0.0001786465,0.00009957462,0.00002758383,0.0002098067,5.805079e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000305978,"about_ca_system_score_gemma":0.00003211836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001651013,"about_ca_topic_score_gemma":0.000002109584,"domain_scores_codex":[0.99887,0.00006134856,0.0002534338,0.0004386971,0.0001679288,0.0002086037],"domain_scores_gemma":[0.9994035,0.0001494942,0.00004871502,0.0001731553,0.000119782,0.000105293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002027485,0.0001857927,0.005378568,0.0006083574,0.00009526219,0.000006285421,0.0003316725,1.895787e-7,0.08679496,0.0007265814,0.0004628632,0.9052067],"study_design_scores_gemma":[0.01290736,0.009380872,0.5378559,0.007611499,0.0006295004,0.001033949,0.0006739635,0.2526273,0.006371827,0.006659296,0.163214,0.001034585],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02915924,0.001498458,0.9644834,0.003069191,0.0002730358,0.001227846,0.000009680894,0.0002370716,0.00004208094],"genre_scores_gemma":[0.1326961,0.00009373218,0.866069,0.0002535789,0.0003507224,0.0002206195,0.000230017,0.00002082967,0.00006542046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9041721,"threshold_uncertainty_score":0.4938033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1177975500242139,"score_gpt":0.4103290538438835,"score_spread":0.2925315038196696,"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."}}