{"id":"W3174526153","doi":"10.1609/aaai.v35i1.16138","title":"A Hierarchical Approach to Multi-Event Survival Analysis","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Science Foundation","keywords":"Event (particle physics); Consistency (knowledge bases); Covariate; Survival analysis; Statistics; Computer science; Econometrics; Mathematics; Artificial intelligence","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":[0.01003829,0.001268027,0.001821713,0.00396922,0.00111641,0.001837922,0.003567584,0.001750061,0.005141006],"category_scores_gemma":[0.02196644,0.0006922741,0.003802021,0.003591723,0.0008846957,0.001939759,0.002554178,0.003457537,0.001955134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001772969,"about_ca_system_score_gemma":0.002834315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02306231,"about_ca_topic_score_gemma":0.03319656,"domain_scores_codex":[0.9943144,0.0024174,0.0004071637,0.001581799,0.0009119055,0.00036736],"domain_scores_gemma":[0.9868724,0.008231923,0.001149831,0.001798065,0.001471718,0.0004760187],"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":[0.0005400514,0.0003703756,0.05096397,0.0007814711,0.00179636,0.0008193614,0.001185531,0.3992244,0.003028853,0.05697205,0.02784911,0.4564685],"study_design_scores_gemma":[0.00003932464,0.00008806619,0.006136761,0.00007214763,0.0001836098,0.0001866765,0.00009156115,0.9096603,0.0005690784,0.07544313,0.007460827,0.00006855103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008097435,0.0009495225,0.9854251,0.0005153364,0.0001250817,0.000157215,0.001919837,0.001919411,0.0008910127],"genre_scores_gemma":[0.3622923,0.0009992442,0.6194428,0.0007728504,0.0006328223,0.0007416715,0.009430752,0.0005857826,0.005101723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02306231,"threshold_uncertainty_score":0.05308825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1174746467560113,"score_gpt":0.3532255714294694,"score_spread":0.2357509246734581,"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."}}