{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001011839,0.000227711,0.0004787425,0.0003945071,0.0001576905,0.00008576863,0.0002581512,0.00007937106,0.00004121866],"category_scores_gemma":[0.003849029,0.0002017636,0.0001321351,0.00242421,0.00003316546,0.00006783161,0.000072965,0.0002086204,0.00001111126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002272904,"about_ca_system_score_gemma":0.0002528029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005186065,"about_ca_topic_score_gemma":0.00001673822,"domain_scores_codex":[0.9976953,0.00009337164,0.0006321165,0.0004653404,0.0003491187,0.0007647656],"domain_scores_gemma":[0.9981456,0.0008188585,0.0002071528,0.0002282877,0.0002070964,0.0003929985],"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":[0.000567817,0.0009167143,0.003030224,0.002766796,0.0002936755,0.00003659257,0.001377813,0.0001132297,0.0009647172,0.4196581,0.005214452,0.5650598],"study_design_scores_gemma":[0.0008476974,0.0009657231,0.0001163511,0.00004473634,0.00003738732,0.000004138626,0.00003897141,0.8067457,0.0001704817,0.1895511,0.001136217,0.0003414484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001488852,0.0004594314,0.9955878,0.001348242,0.0001647793,0.0005597979,0.0002280245,0.0001223668,0.00004071562],"genre_scores_gemma":[0.2221855,0.0002062842,0.7770748,0.0003077101,0.0001003886,0.00003467693,0.00001466672,0.00004230839,0.00003365615],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8066325,"threshold_uncertainty_score":0.8227682,"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."}}