{"id":"W4406220600","doi":"10.3390/electronics14020248","title":"Deep Audio Features and Self-Supervised Learning for Early Diagnosis of Neonatal Diseases: Sepsis and Respiratory Distress Syndrome Classification from Infant Cry Signals","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Infant Health and Development","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Respiratory distress; Acute respiratory distress; Sepsis; Distress; Neonatal sepsis; Medicine; Artificial intelligence; Respiratory system; Deep learning; Computer science; Speech recognition; Audiology; Intensive care medicine; Internal medicine; Lung; Surgery; Clinical psychology","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":[0.0003698391,0.0001859299,0.0003734206,0.0001426232,0.0007341831,0.00002029408,0.0001185948,0.0002374149,0.00003443684],"category_scores_gemma":[0.0003323526,0.0001748352,0.00005032823,0.0002015392,0.00005132726,0.0001217623,0.00009289313,0.0005082,0.000002781894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001746169,"about_ca_system_score_gemma":0.0007993648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001132274,"about_ca_topic_score_gemma":0.0002403138,"domain_scores_codex":[0.998045,0.0003040588,0.0005326524,0.0003822993,0.0001806369,0.0005553662],"domain_scores_gemma":[0.9977967,0.001426182,0.0002391647,0.0002092549,0.0001597199,0.0001689475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004656705,0.0001116259,0.9501266,0.001652725,0.0002196317,0.000004869039,0.00542237,0.00001359672,0.0002485503,0.002450137,0.003479578,0.03580461],"study_design_scores_gemma":[0.001674076,0.0002903699,0.9504341,0.0003947794,0.0001352704,7.602131e-7,0.0008873728,0.001135818,0.0001789882,0.00138195,0.04325572,0.00023083],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580308,0.03792674,0.001494941,0.0007898156,0.0001474734,0.001265796,0.0001207078,0.00008983271,0.0001339036],"genre_scores_gemma":[0.9924157,0.003857362,0.001372441,0.001046643,0.000056564,0.0008849354,0.0001844349,0.00002523802,0.0001567092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03977615,"threshold_uncertainty_score":0.7129572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01699397406882034,"score_gpt":0.330756968729248,"score_spread":0.3137629946604276,"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."}}