{"id":"W3109500069","doi":"10.1016/j.compbiomed.2020.104142","title":"Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning","year":2020,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Michael J. Fox Foundation for Parkinson's Research","keywords":"Radiomics; Identification (biology); Parkinson's disease; Machine learning; Artificial intelligence; Computer science; Disease; Medicine; Pathology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001402622,0.0001137475,0.0003184571,0.00009867688,0.00004754359,0.000002599425,0.00003699887,0.00003964151,0.000008416607],"category_scores_gemma":[0.0001088837,0.00008959652,0.00002134843,0.00009585424,0.0001966982,0.00002771688,0.00004298101,0.0001078213,4.694338e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001738198,"about_ca_system_score_gemma":0.00002670285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002874968,"about_ca_topic_score_gemma":0.000001049258,"domain_scores_codex":[0.9992648,0.00006113049,0.0002248605,0.0002757629,0.00005364281,0.00011985],"domain_scores_gemma":[0.9995158,0.00005914454,0.0001011943,0.00008368811,0.00002413653,0.0002160278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009686747,0.0001211209,0.9603159,0.0003196523,0.000138635,0.0002183993,0.0004618356,0.0001659904,0.01602959,0.000943843,0.00006853472,0.02024783],"study_design_scores_gemma":[0.008267753,0.001038419,0.6165056,0.0004495991,0.0005243995,0.0001174944,0.000129509,0.3638721,0.001404417,0.001369464,0.006085151,0.0002361359],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733995,0.008514951,0.01613583,0.001586046,0.0001341868,0.0001795998,0.00001511164,0.00002026753,0.00001452902],"genre_scores_gemma":[0.9959008,0.001701638,0.001661643,0.0005151859,0.00009918436,0.000002768736,0.0001047579,0.000008225946,0.000005781897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3637061,"threshold_uncertainty_score":0.365364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03620372923167545,"score_gpt":0.2890472018985741,"score_spread":0.2528434726668986,"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."}}