{"id":"W1976265573","doi":"10.1109/eeei.2012.6377065","title":"Early diagnosis of Parkinson's disease via machine learning on speech data","year":2012,"lang":"en","type":"article","venue":"","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health","keywords":"Disease; Feature (linguistics); Computer science; Parkinson's disease; Training set; Range (aeronautics); Process (computing); Artificial intelligence; Machine learning; Natural language processing; Speech recognition; Audiology; Medicine; Linguistics; Engineering; Pathology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000229843,0.0001175012,0.0002038111,0.00008095115,0.00004000453,0.00000676896,0.0001599271,0.00004289805,0.00137194],"category_scores_gemma":[0.000292733,0.00008972021,0.00005759343,0.0001315197,0.0000359113,0.0001916262,0.0001053419,0.0001861098,0.0002541505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001290382,"about_ca_system_score_gemma":0.00002356412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004460109,"about_ca_topic_score_gemma":0.00002844046,"domain_scores_codex":[0.9990454,0.00003613884,0.0001616317,0.0002013674,0.0002948739,0.0002605941],"domain_scores_gemma":[0.9988928,0.00009357722,0.00005391895,0.0006367037,0.00002877023,0.0002941968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000152002,0.0004973949,0.9618241,0.00005435538,0.00004427354,0.00001475253,0.00008249396,0.000001405058,0.0001385185,0.00007358781,0.003860968,0.03325612],"study_design_scores_gemma":[0.0009683658,0.0002601081,0.8211129,0.00007075974,0.0001814576,0.000003993105,0.0000785759,0.0004878752,0.001897705,0.00002499944,0.1747715,0.0001417483],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987708,0.001080351,0.0001714208,0.001896312,0.00009226292,0.0002152368,0.00002985354,0.00006921049,0.0087373],"genre_scores_gemma":[0.9957006,0.0003586219,0.0006847676,0.0007403798,0.0001487219,0.000008913447,0.0001987129,0.00002235139,0.002136917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1709105,"threshold_uncertainty_score":0.9995409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984419257531877,"score_gpt":0.2980816646793719,"score_spread":0.2582374721040531,"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."}}