{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002272014,0.001071702,0.001000704,0.002027845,0.0003080181,0.0008605591,0.0008551903,0.0009051093,0.001111462],"category_scores_gemma":[0.007526479,0.0002852333,0.00116705,0.001380144,0.0005077234,0.001101569,0.0007906574,0.001541972,0.0007050292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000429848,"about_ca_system_score_gemma":0.0004325999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001788112,"about_ca_topic_score_gemma":0.001059381,"domain_scores_codex":[0.9982775,0.0004395063,0.0002038106,0.0004468136,0.0005434136,0.00008890216],"domain_scores_gemma":[0.9961084,0.002445385,0.0003473569,0.0004212336,0.0006104952,0.00006720929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00013979,0.0001258327,0.003540485,0.0001668625,0.000158045,0.0001246679,0.00007396011,0.07713893,0.01434113,0.002252819,0.002013256,0.8999243],"study_design_scores_gemma":[0.00002240287,0.0002082844,0.004884009,0.00006623247,0.00006235603,0.0002939458,0.0000322328,0.9711425,0.01229264,0.006864298,0.004078836,0.00005231491],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01467664,0.003060348,0.980194,0.000351543,0.00008817526,0.00004447757,0.0001411502,0.001052329,0.0003913927],"genre_scores_gemma":[0.3714467,0.002091414,0.6233129,0.0002840991,0.0002903122,0.0001913172,0.0008607185,0.0001609617,0.001361564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002272014,"threshold_uncertainty_score":0.0120157,"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."}}