{"id":"W4415609969","doi":"10.3390/e27111109","title":"Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm","year":2025,"lang":"en","type":"article","venue":"Entropy","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"","keywords":"Bayesian optimization; Artificial neural network; Pattern recognition (psychology); Deep neural networks; Bayesian probability; Mel-frequency cepstrum; Feedforward neural network; Cepstrum; Random search","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":[0.001126086,0.0008405425,0.0005164975,0.0005625091,0.0001792965,0.0003573337,0.000518692,0.0007028168,0.00053205],"category_scores_gemma":[0.002434044,0.0002535905,0.0004218519,0.0002178558,0.0002679124,0.0005164482,0.0004196057,0.0005627692,0.0001913559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004122171,"about_ca_system_score_gemma":0.0006156053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005033672,"about_ca_topic_score_gemma":0.006416036,"domain_scores_codex":[0.999693,0.00008836118,0.00002714477,0.00008251349,0.00006370775,0.00004523565],"domain_scores_gemma":[0.9994312,0.0003254575,0.00006971,0.00002894807,0.0001178104,0.000026826],"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":[0.0006669713,0.0003865479,0.02354568,0.0001560384,0.0002106327,0.0002449303,0.0001128114,0.4624493,0.03374168,0.001302768,0.001915904,0.4752667],"study_design_scores_gemma":[0.000008300074,0.00007532188,0.002521226,0.00001176921,0.00001771042,0.00004808258,0.00001311486,0.9923688,0.004369215,0.0004121812,0.0001453754,0.00000900353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4855502,0.001810975,0.5085936,0.0004210058,0.0001005497,0.00008665596,0.0002463998,0.001046868,0.002143695],"genre_scores_gemma":[0.9180679,0.000251057,0.07979503,0.0001500375,0.00002324353,0.0000596852,0.0003591112,0.00002803815,0.001265881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005033672,"threshold_uncertainty_score":0.01000875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006154211650128722,"score_gpt":0.2756007656925397,"score_spread":0.2694465540424109,"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."}}