{"id":"W4399709995","doi":"10.1016/j.media.2024.103231","title":"Interpretable deep clustering survival machines for Alzheimer’s disease subtype discovery","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Genentech; IXICO; U.S. National Library of Medicine; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of Southern California; Biogen; BioClinica; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Cluster analysis; Discriminative model; Artificial intelligence; Machine learning; Computer science; Categorization; Pattern recognition (psychology); Data mining","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":[0.002509265,0.001536489,0.001280778,0.002040412,0.0008971795,0.001480443,0.002115504,0.002357765,0.003543975],"category_scores_gemma":[0.006758129,0.0005731857,0.001863514,0.001353566,0.0005196713,0.001119396,0.001517795,0.002589179,0.002815893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275481,"about_ca_system_score_gemma":0.002071951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01110399,"about_ca_topic_score_gemma":0.0214273,"domain_scores_codex":[0.9990972,0.0002586641,0.00008157697,0.0002779261,0.0001560018,0.0001285636],"domain_scores_gemma":[0.99712,0.001439837,0.0001772196,0.0005084416,0.0006272808,0.0001272884],"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":[0.0006948056,0.0004106629,0.01489617,0.0001965683,0.0003469722,0.0003353792,0.000262283,0.132578,0.005726284,0.007324783,0.04432005,0.7929079],"study_design_scores_gemma":[0.00002733039,0.0000663144,0.001465516,0.00005337341,0.00005259597,0.0001137044,0.00006398265,0.9730229,0.002239735,0.02038013,0.002497321,0.00001702966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1048799,0.003088751,0.8656522,0.002335724,0.0003866965,0.0002725759,0.006788272,0.01318353,0.003412308],"genre_scores_gemma":[0.6387535,0.0009458606,0.3287893,0.001022883,0.0003623077,0.0004053959,0.01840359,0.0006358637,0.01068136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01110399,"threshold_uncertainty_score":0.02207869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921036101325226,"score_gpt":0.3603439240123461,"score_spread":0.3411335629990939,"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."}}