{"id":"W3027993156","doi":"10.1007/s10115-020-01472-1","title":"Survival neural networks for time-to-event prediction in longitudinal study","year":2020,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"National Institute of Justice; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Censoring (clinical trials); Covariate; Survival analysis; Artificial neural network; Computer science; Event (particle physics); Accelerated failure time model; Prognostics; Brier score; Artificial intelligence; Machine learning; Statistics; Data mining; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01139732,0.0008347084,0.001841208,0.002115881,0.0006932853,0.001275715,0.002248569,0.002077869,0.003212702],"category_scores_gemma":[0.04095465,0.0006656621,0.0009780038,0.002008753,0.001104197,0.002414202,0.001354013,0.002914331,0.0005860517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001346471,"about_ca_system_score_gemma":0.001388248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01420861,"about_ca_topic_score_gemma":0.01178368,"domain_scores_codex":[0.9981621,0.001073825,0.0001415504,0.0003574886,0.0001539817,0.0001110718],"domain_scores_gemma":[0.9587115,0.03794772,0.001040037,0.001061585,0.000922262,0.0003168139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004607086,0.0002448965,0.01570112,0.0002994692,0.0005206149,0.0001911086,0.0002133494,0.739401,0.0003309061,0.05796012,0.004110506,0.1805662],"study_design_scores_gemma":[0.0000104356,0.00001586908,0.0007448884,0.00002831743,0.00003651431,0.00001379495,0.00001327984,0.9644328,0.00007291057,0.03438383,0.0002386734,0.000008632525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05310623,0.004679719,0.9374929,0.002105991,0.0002107975,0.00008832203,0.0009075765,0.0004857309,0.0009226718],"genre_scores_gemma":[0.863917,0.00391881,0.122167,0.0003886597,0.0005145321,0.0005213923,0.001892271,0.00007992577,0.00660053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01420861,"threshold_uncertainty_score":0.0602755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09601427600990584,"score_gpt":0.3584811893582615,"score_spread":0.2624669133483556,"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."}}