{"id":"W4230021958","doi":"10.31219/osf.io/ryz83","title":"Using Machine Intelligence to Uncover Alzheimer’s Disease Progression Heterogeneity","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Disease; Computer science; Data science; Set (abstract data type); Precision medicine; Machine learning; Artificial intelligence; Medicine; Pathology","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":[0.002041007,0.0004990301,0.0005954822,0.003323816,0.000433253,0.002345659,0.0003675291,0.000554763,0.0007495893],"category_scores_gemma":[0.005435443,0.0002371215,0.0006151892,0.002186694,0.001338434,0.001670946,0.0009971049,0.00111162,0.0002105295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007064635,"about_ca_system_score_gemma":0.0006141269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009868363,"about_ca_topic_score_gemma":0.0008822818,"domain_scores_codex":[0.999199,0.0003424445,0.00003540017,0.0001859665,0.0001788101,0.00005843558],"domain_scores_gemma":[0.9971806,0.001933365,0.0003554727,0.0003486701,0.0001072382,0.00007477331],"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":[0.0003206134,0.0002382033,0.1122895,0.0006624545,0.0007483833,0.001270479,0.002319656,0.1510216,0.02685075,0.305962,0.006106796,0.3922094],"study_design_scores_gemma":[0.00001966878,0.00006565764,0.02394459,0.00009167149,0.00009602976,0.0002812219,0.0004214885,0.3256432,0.004730568,0.6367243,0.007933945,0.00004766211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3209797,0.007972343,0.6505045,0.005784685,0.0001528868,0.0001155103,0.001814573,0.001035693,0.01164007],"genre_scores_gemma":[0.8240473,0.002292558,0.1708402,0.0003546203,0.0001729531,0.00007417709,0.001223341,0.0000715421,0.0009232531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003323816,"threshold_uncertainty_score":0.01079404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05796236011986408,"score_gpt":0.3423813104995559,"score_spread":0.2844189503796919,"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."}}