{"id":"W4303453972","doi":"10.21203/rs.3.rs-1813123/v1","title":"An Ontology-based approach for Modelling and Querying Alzheimer’s Disease Data","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"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; H. Lundbeck A/S; Servier; Novartis Pharmaceuticals Corporation; Regione Lazio; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; European Regional Development Fund; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Ontology; Computer science; Disease; Information retrieval; Data science; Medicine; Epistemology; Philosophy; Internal medicine","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":[0.004237243,0.0008955235,0.001106048,0.004672408,0.00134784,0.00580062,0.002555568,0.00176145,0.001978471],"category_scores_gemma":[0.008270092,0.0008119398,0.003331247,0.005718609,0.0009045678,0.006636201,0.003536592,0.002473856,0.0009400267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001915819,"about_ca_system_score_gemma":0.003158387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02211036,"about_ca_topic_score_gemma":0.03249111,"domain_scores_codex":[0.9970498,0.000493974,0.0006807485,0.0004612641,0.001172396,0.0001418202],"domain_scores_gemma":[0.99699,0.001304754,0.000211039,0.0007577458,0.0005646856,0.0001718034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006668646,0.0009020457,0.008242174,0.002041647,0.001195624,0.002196303,0.003792593,0.08401729,0.0268968,0.3363028,0.04909351,0.4846524],"study_design_scores_gemma":[0.0001321986,0.00009175239,0.002543363,0.0004382228,0.0006067841,0.0008713102,0.001137605,0.5046539,0.01494935,0.3169802,0.1574516,0.0001436824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006884447,0.0003641218,0.9769641,0.001092677,0.00007671979,0.0003130017,0.005807005,0.006399376,0.002098567],"genre_scores_gemma":[0.06672575,0.0006373138,0.9158628,0.0004342147,0.00004814451,0.0003313533,0.01316515,0.0005855168,0.002209728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02211036,"threshold_uncertainty_score":0.04396337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3089350076466285,"score_gpt":0.4669794441964446,"score_spread":0.1580444365498161,"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."}}