{"id":"W4306820107","doi":"10.48550/arxiv.2210.08385","title":"A Joint Modeling Approach for Clustering Mixed-Type Multivariate Longitudinal Data: Application to the CHILD Cohort Study","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Categorical variable; Cluster analysis; Longitudinal study; Cohort; Markov chain Monte Carlo; Statistics; Multivariate analysis; Computer science; Cluster (spacecraft); Mixed model; Data mining; Monte Carlo method; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02229024,0.0009890764,0.001223811,0.001857305,0.001474712,0.001515763,0.002991145,0.001578701,0.002761536],"category_scores_gemma":[0.03535631,0.0008489459,0.002412662,0.002547774,0.001133475,0.001395306,0.002438942,0.002934291,0.0006767994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001782655,"about_ca_system_score_gemma":0.002698755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03079673,"about_ca_topic_score_gemma":0.03284776,"domain_scores_codex":[0.9928216,0.005568948,0.0002088633,0.0007075134,0.0005402633,0.0001527155],"domain_scores_gemma":[0.9834346,0.01275141,0.0009280912,0.001407489,0.001105392,0.0003729371],"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":[0.0006192398,0.0001941173,0.03221003,0.0003399624,0.0009590749,0.0005798789,0.001639242,0.5058569,0.001950903,0.2529868,0.008775304,0.1938885],"study_design_scores_gemma":[0.00004971349,0.00004755236,0.00206449,0.00003178206,0.00005921102,0.0001407834,0.00008531203,0.9234589,0.000260836,0.06998168,0.003767018,0.00005273997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005323585,0.0001407886,0.993641,0.0002421535,0.00001817014,0.0000607053,0.0002085902,0.0001993782,0.0001656355],"genre_scores_gemma":[0.1123141,0.0003498027,0.8838843,0.0001389368,0.00006853603,0.0006849288,0.0008500749,0.0002376412,0.001471584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03079673,"threshold_uncertainty_score":0.1178835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1838334589808447,"score_gpt":0.2604311135505951,"score_spread":0.07659765456975043,"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."}}