{"id":"W4293531581","doi":"10.3390/e24091194","title":"An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems","year":2022,"lang":"en","type":"article","venue":"Entropy","topic":"Infant Health and Development","field":"Health Professions","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Bill and Melinda Gates Foundation","keywords":"Mel-frequency cepstrum; Support vector machine; Artificial intelligence; Computer science; Pattern recognition (psychology); Cepstrum; Centroid; Approximate entropy; Entropy (arrow of time); Feature vector; Speech recognition; Feature selection; Hyperparameter; Feature extraction","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":[0.000777691,0.0001351737,0.000293139,0.000264201,0.0006413356,0.000004683604,0.0001588683,0.0000949973,0.0002336581],"category_scores_gemma":[0.0003824636,0.0001286294,0.000058873,0.0002779526,0.00001822069,0.00003655022,0.00004353819,0.0005129374,0.00001610175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004603395,"about_ca_system_score_gemma":0.0006494597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000725336,"about_ca_topic_score_gemma":0.000192258,"domain_scores_codex":[0.9974532,0.000768025,0.0006485997,0.0002634426,0.0002942329,0.0005724836],"domain_scores_gemma":[0.9978741,0.001333835,0.0002850156,0.0002736266,0.00008213332,0.0001512826],"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":[0.01084902,0.00250454,0.6045448,0.01289315,0.0001730464,0.0001123162,0.05431005,0.1641324,0.06553275,0.02211003,0.01970691,0.04313103],"study_design_scores_gemma":[0.01786694,0.00349426,0.2034933,0.0008718168,0.0001014627,0.000009280174,0.01999248,0.06212391,0.004640514,0.003053941,0.6832414,0.00111072],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9240474,0.0004256433,0.06621583,0.001121181,0.003153446,0.004417609,0.0002857121,0.000121683,0.0002115001],"genre_scores_gemma":[0.9937435,0.000009010441,0.002198718,0.00118022,0.0002443954,0.00239187,0.0001116253,0.00002830894,0.00009231592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6635345,"threshold_uncertainty_score":0.5245353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0222388565390157,"score_gpt":0.3532806364400745,"score_spread":0.3310417799010588,"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."}}