{"id":"W3010692414","doi":"10.1111/biom.13261","title":"Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Markov chain Monte Carlo; Computer science; Covariate; Bayesian probability; Inference; Bayesian inference; Missing data; Data mining; Machine learning; Artificial intelligence; Econometrics; 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":[0.01470399,0.0008596813,0.001943326,0.001796696,0.0009327684,0.002079545,0.003696627,0.002171938,0.003412028],"category_scores_gemma":[0.0384234,0.001530536,0.001795461,0.002843778,0.001827999,0.003415526,0.002542222,0.003428799,0.0007020122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002241491,"about_ca_system_score_gemma":0.002192036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01801757,"about_ca_topic_score_gemma":0.02094416,"domain_scores_codex":[0.9945727,0.003652211,0.0002338549,0.0007609571,0.0005191814,0.0002611777],"domain_scores_gemma":[0.9715556,0.02460704,0.001745766,0.001049545,0.0007284025,0.0003137254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000199746,0.0001227982,0.005412086,0.000130823,0.0002027935,0.0001577289,0.0003805679,0.6829641,0.0003151077,0.2749177,0.001869174,0.03332733],"study_design_scores_gemma":[0.00001868244,0.00001261759,0.0004961039,0.00001820054,0.00001378003,0.00001521945,0.00001722052,0.9372702,0.00004674372,0.06150972,0.0005664809,0.00001499196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01331248,0.0003514911,0.9843322,0.0006601095,0.00003308782,0.00006556377,0.0004404891,0.0002084552,0.0005961361],"genre_scores_gemma":[0.5622764,0.001550245,0.4228257,0.0003807018,0.0002407653,0.001315178,0.002688362,0.0001644403,0.008558186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01801757,"threshold_uncertainty_score":0.07776308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2451168004090064,"score_gpt":0.416720227967442,"score_spread":0.1716034275584356,"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."}}