{"id":"W3157400911","doi":"10.1016/j.cmpb.2021.106131","title":"Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson's disease","year":2021,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Machine learning; Computer science; Feature selection; Identification (biology); Cluster analysis; Feature (linguistics); Task (project management); Pattern recognition (psychology); Selection (genetic algorithm)","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":[0.004528212,0.0005899489,0.001339815,0.002228764,0.0005796009,0.001272462,0.0007720105,0.0006542142,0.0009429165],"category_scores_gemma":[0.008884337,0.000307719,0.001320045,0.001477123,0.0003547749,0.0006579743,0.0006151572,0.001084059,0.0002655995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005601622,"about_ca_system_score_gemma":0.0009628242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005936624,"about_ca_topic_score_gemma":0.004358304,"domain_scores_codex":[0.9987209,0.0006320772,0.0001813743,0.0001802255,0.0001865244,0.00009880987],"domain_scores_gemma":[0.9953441,0.00377226,0.0001731432,0.0001810974,0.000454726,0.00007467219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001427017,0.0003913469,0.05111947,0.000186058,0.0004235771,0.0002814466,0.0001581599,0.09812435,0.005543011,0.002564252,0.004976626,0.8348047],"study_design_scores_gemma":[0.00007822696,0.0001481125,0.01989716,0.00003031695,0.0001219084,0.0001696828,0.0000706545,0.9716897,0.001708412,0.005484447,0.0005733934,0.00002790119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3335147,0.004984543,0.6563366,0.001111428,0.0002107214,0.0001904619,0.00138518,0.001027808,0.001238571],"genre_scores_gemma":[0.8552756,0.000706801,0.1411679,0.0001051846,0.000146855,0.0001938155,0.001244637,0.00008340069,0.001075847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005936624,"threshold_uncertainty_score":0.02394778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04420528982322956,"score_gpt":0.375434078945144,"score_spread":0.3312287891219144,"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."}}