{"id":"W4402215565","doi":"10.1109/jbhi.2024.3454550","title":"WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Disease; Speech recognition; Natural language processing; Patient privacy; Artificial intelligence; Internet privacy; Health care; Medicine; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001842086,0.001016426,0.0007391368,0.0004802006,0.000225032,0.0009434997,0.001557024,0.001353668,0.001934108],"category_scores_gemma":[0.004461295,0.0003881911,0.000991317,0.0002633154,0.0007803087,0.001335105,0.001542809,0.002005427,0.0009001486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006778076,"about_ca_system_score_gemma":0.0009932327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003243872,"about_ca_topic_score_gemma":0.003499444,"domain_scores_codex":[0.9992372,0.0002761904,0.00004359685,0.0002675745,0.000119513,0.00005597146],"domain_scores_gemma":[0.9989671,0.0005783449,0.000090504,0.000176991,0.0001448193,0.00004226924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000839687,0.0002399603,0.004642954,0.0003162595,0.000274359,0.0005912609,0.0004454227,0.6331564,0.01586892,0.02578219,0.01255065,0.3052919],"study_design_scores_gemma":[0.00002356374,0.0001489261,0.0005870432,0.00002624347,0.00003709792,0.0002173958,0.0000227358,0.98396,0.001908708,0.01073904,0.002309289,0.00001996482],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02755143,0.0009670455,0.964059,0.001080429,0.0001718896,0.0001349331,0.001339405,0.003252937,0.001442887],"genre_scores_gemma":[0.7404559,0.001160272,0.2399731,0.001279221,0.0002513821,0.0005365491,0.004692611,0.0004162421,0.01123471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003243872,"threshold_uncertainty_score":0.009741962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0453105667309551,"score_gpt":0.3485969643707361,"score_spread":0.303286397639781,"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."}}