{"id":"W3113201251","doi":"10.1002/alz.046742","title":"Machine learning analysis of speech and eye tracking data to distinguish Alzheimer's clinic patients from healthy controls","year":2020,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Audiology; Logistic regression; Eye tracking; Cognition; Disease; Psychology; Internal medicine; Artificial intelligence; Psychiatry; Computer science","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":[0.002184166,0.0006723874,0.0004821017,0.002141411,0.0002613517,0.0006743515,0.0003511781,0.0005128354,0.00139193],"category_scores_gemma":[0.007263456,0.000106524,0.0005928967,0.0006444529,0.000224938,0.0002632755,0.0003176287,0.0003288754,0.000563465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003043941,"about_ca_system_score_gemma":0.0002611796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002721469,"about_ca_topic_score_gemma":0.002589247,"domain_scores_codex":[0.9988537,0.0004992556,0.0001376068,0.0002934595,0.0001269013,0.0000889678],"domain_scores_gemma":[0.9954771,0.003076661,0.000399686,0.0003539036,0.0005393502,0.000153265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002362107,0.0004673463,0.8658757,0.00009330401,0.0004448,0.0002762593,0.0003124237,0.008575668,0.01113416,0.0001475778,0.001006324,0.1093043],"study_design_scores_gemma":[0.00008551594,0.0009629016,0.8113292,0.00005242272,0.0002296696,0.0008103674,0.000333547,0.1782683,0.006630779,0.0005784232,0.0006671841,0.00005169732],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921864,0.0002248172,0.005984699,0.00005937806,0.00001735806,0.00004746635,0.0008435521,0.00009240354,0.0005438962],"genre_scores_gemma":[0.9963915,0.00004284237,0.002435274,0.00001744761,0.00001217089,0.00003566484,0.0009201164,0.00000517374,0.0001399765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002721469,"threshold_uncertainty_score":0.01155114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08947830436235067,"score_gpt":0.3800999792127868,"score_spread":0.2906216748504362,"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."}}