{"id":"W3024363903","doi":"10.2196/13611","title":"Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques","year":2020,"lang":"en","type":"article","venue":"JMIR Biomedical Engineering","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian National University; Australian Government","keywords":"Phonation; Computer science; Support vector machine; Feature (linguistics); Data set; Set (abstract data type); Artificial intelligence; Feature engineering; Machine learning; Pattern recognition (psychology); Deep learning; Medicine; Audiology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009634978,0.0002906944,0.0003757237,0.000159781,0.00004866491,0.00003334695,0.000147008,0.0001968179,0.00008012805],"category_scores_gemma":[0.0008656341,0.0002660545,0.0002056456,0.0004290685,0.00002182752,0.00009531713,0.00005964884,0.0005793854,0.00001235875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005628604,"about_ca_system_score_gemma":0.00008664213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008685608,"about_ca_topic_score_gemma":5.670457e-7,"domain_scores_codex":[0.9985602,0.000006444399,0.0002467301,0.0003891117,0.0003419776,0.0004554698],"domain_scores_gemma":[0.9984974,0.00009312358,0.0000407912,0.0001646159,0.00003235471,0.001171693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002760066,0.001778815,0.04373768,0.02025682,0.001671945,0.002022567,0.003280022,0.03784204,0.1285722,0.0009636608,0.3904501,0.3666641],"study_design_scores_gemma":[0.001060523,0.0003447573,0.002029613,0.0003814758,0.00008777541,0.000009378927,0.00002105691,0.1598209,0.001722091,0.000002834969,0.8342282,0.0002913362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05005072,0.01661822,0.7497742,0.1693989,0.0008755568,0.005635263,0.000156732,0.007330812,0.0001595772],"genre_scores_gemma":[0.7558068,0.002172797,0.2204868,0.00710264,0.008188946,0.002702754,0.001385597,0.000639679,0.001513952],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7057561,"threshold_uncertainty_score":0.9999791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736119203992213,"score_gpt":0.2454880148594546,"score_spread":0.2281268228195325,"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."}}