{"id":"W2509318466","doi":"10.18653/v1/w16-0302","title":"Towards Early Dementia Detection: Fusing Linguistic and Non-Linguistic Clinical Data","year":2016,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Synarc; University of Southern California; Medpace; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Foundation for the National Institutes of Health","keywords":"Computer science; Dementia; Linguistics; Natural language processing; Linguistic analysis; Artificial intelligence; Deep linguistic processing; Medicine; Disease; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00973621,0.001695342,0.001270097,0.008709177,0.0006568729,0.003756584,0.001057559,0.001350458,0.001497936],"category_scores_gemma":[0.03624082,0.0005137473,0.001532445,0.004495447,0.0005419467,0.005191035,0.002830821,0.001787518,0.001276157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006994738,"about_ca_system_score_gemma":0.001627185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006702787,"about_ca_topic_score_gemma":0.008479552,"domain_scores_codex":[0.9953442,0.002920396,0.0004035019,0.0007219182,0.0003923596,0.000217523],"domain_scores_gemma":[0.9733376,0.02175374,0.001419311,0.001305343,0.001755631,0.0004283504],"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":[0.001678645,0.001085817,0.132341,0.001650975,0.0009226751,0.0007406278,0.003531642,0.02042431,0.01464896,0.003116893,0.00817852,0.8116798],"study_design_scores_gemma":[0.0001550499,0.0009224453,0.1168319,0.001268254,0.001774266,0.001239786,0.007007772,0.7623934,0.01480079,0.07036334,0.02285487,0.0003882412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3997503,0.01113061,0.5592828,0.006779139,0.0005192118,0.0008242477,0.008396269,0.004833509,0.008483923],"genre_scores_gemma":[0.7814817,0.002288952,0.203852,0.0005332655,0.0002822635,0.000300159,0.009448946,0.0001574952,0.001655171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00973621,"threshold_uncertainty_score":0.0514906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09277553604152745,"score_gpt":0.3442513983840846,"score_spread":0.2514758623425572,"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."}}