{"id":"W2900225999","doi":"10.1007/s41109-018-0101-4","title":"Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data","year":2018,"lang":"en","type":"article","venue":"Applied Network Science","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"James S. McDonnell Foundation","keywords":"Comorbidity; Computer science; Data science; Longitudinal data; Metropolitan area; Null hypothesis; Focus (optics); Data mining; Machine learning; Medicine; Econometrics; Psychiatry; Mathematics; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01760524,0.001140484,0.001107067,0.008521684,0.0009088425,0.001739729,0.002341194,0.0009772019,0.005589671],"category_scores_gemma":[0.09083838,0.0007201082,0.001929418,0.008433273,0.0009500997,0.001948964,0.002000144,0.002969403,0.001014306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001286835,"about_ca_system_score_gemma":0.002037447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005418418,"about_ca_topic_score_gemma":0.006692433,"domain_scores_codex":[0.9913759,0.006297629,0.0005225685,0.0007987547,0.0008943884,0.0001108001],"domain_scores_gemma":[0.9185622,0.06926948,0.004510505,0.004868923,0.002366695,0.0004222954],"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":[0.000282099,0.0002498186,0.03744818,0.001196331,0.002258965,0.0003746518,0.0008284766,0.2629071,0.001120704,0.2389193,0.01586998,0.4385443],"study_design_scores_gemma":[0.00007166876,0.00007084048,0.005600845,0.0001552199,0.0001450233,0.0001400933,0.0001516662,0.6720155,0.0004221356,0.3134201,0.00775912,0.00004772006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00386316,0.0002358512,0.9931391,0.0002774767,0.00004673519,0.000249196,0.001402792,0.0003550782,0.0004306222],"genre_scores_gemma":[0.1090877,0.0011554,0.8772497,0.0002350581,0.0003208239,0.003770575,0.006582024,0.0002078751,0.001390851],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01760524,"threshold_uncertainty_score":0.09310645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1369234183367807,"score_gpt":0.4430373273626704,"score_spread":0.3061139090258898,"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."}}