{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00427741,0.001106757,0.001000777,0.002880467,0.0004401137,0.001113642,0.0003709322,0.001032265,0.0007069636],"category_scores_gemma":[0.0146414,0.0002220039,0.001208576,0.001159836,0.0006015718,0.0006463847,0.001006766,0.0009261214,0.0005678991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003777506,"about_ca_system_score_gemma":0.0004319814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00650803,"about_ca_topic_score_gemma":0.006544128,"domain_scores_codex":[0.9959452,0.002385306,0.0004265126,0.0006501602,0.0003329833,0.0002599492],"domain_scores_gemma":[0.9863639,0.01085995,0.0004691675,0.00106886,0.0009411096,0.0002969589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006415602,0.00192536,0.6028057,0.0005471025,0.002014416,0.002261575,0.001834852,0.07230969,0.03923312,0.0004091592,0.002168807,0.2680746],"study_design_scores_gemma":[0.000246332,0.00219447,0.7545056,0.0001368414,0.001015973,0.002179983,0.00159235,0.1914256,0.04306265,0.0009897075,0.002391107,0.0002594628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898736,0.0002168575,0.007744941,0.00008718074,0.00003423548,0.00006862796,0.001268547,0.0001561258,0.0005498542],"genre_scores_gemma":[0.988962,0.00008051044,0.007694595,0.00002520558,0.00001602563,0.00004667018,0.002916518,0.00001919898,0.0002393138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00650803,"threshold_uncertainty_score":0.02262139,"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."}}