{"id":"W4381329044","doi":"10.1109/tbme.2023.3287514","title":"Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Machine Intelligence Institute","keywords":"Feature selection; Computer science; Machine learning; Feature (linguistics); Artificial intelligence; Prior probability; Bayesian probability; Artificial neural network; Data mining; Bayesian inference","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004132584,0.0001918877,0.0002315709,0.000446203,0.000222142,0.0001071176,0.0002853422,0.0001373473,0.000004987927],"category_scores_gemma":[0.00003570026,0.0001926707,0.0000897757,0.001124884,0.00003248588,0.0001494803,0.000009557581,0.0004094146,0.000001997432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007976733,"about_ca_system_score_gemma":0.00003223953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005110194,"about_ca_topic_score_gemma":0.000005671703,"domain_scores_codex":[0.9984827,0.00005927009,0.00025341,0.0004344152,0.0003111341,0.0004590636],"domain_scores_gemma":[0.999011,0.0003573075,0.00003176763,0.0002110069,0.00005181096,0.0003370731],"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":[0.00002441819,0.0000214694,0.00002020556,0.00009956079,0.00003688083,0.000007253157,0.0004028934,0.9614846,0.0008836655,0.000226436,0.0002512493,0.03654142],"study_design_scores_gemma":[0.0003880637,0.0002541098,0.0005059479,0.00008947271,0.00001141389,0.0000430843,0.00001162466,0.9975967,0.0001332489,0.00001694201,0.0007888895,0.0001604552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007713397,0.00004235575,0.9867774,0.001652437,0.002575415,0.0003394933,0.00002545724,0.0008711207,0.000002898862],"genre_scores_gemma":[0.9374987,0.00001908416,0.06175898,0.0002027724,0.0003308362,0.00007176079,0.00001101767,0.00003957142,0.00006723198],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9297854,"threshold_uncertainty_score":0.7856882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042664565802597,"score_gpt":0.2998549349532155,"score_spread":0.2694282892951895,"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."}}