{"id":"W2776711118","doi":"10.14236/jhi.v24i4.962","title":"The Multimorbidity Cluster Analysis Tool: Identifying Combinations and Permutations of Multiple Chronic Diseases Using a Record-Level Computational Analysis","year":2017,"lang":"en","type":"article","venue":"Journal of Innovation in Health Informatics","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Université de Sherbrooke; Western University; Centre for Family Medicine","funders":"","keywords":"Multimorbidity; Executable; Computer science; Data science; Sample (material); Cluster (spacecraft); Medical diagnosis; Diagnosis code; Java; Comorbidity; Health care; Chronic disease; Medicine; Family medicine; Pathology; Population; Programming language","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":[0.00556255,0.00116505,0.001128259,0.004144814,0.001456507,0.002955569,0.002167934,0.0005328699,0.01567253],"category_scores_gemma":[0.03393999,0.0006694934,0.002583744,0.004499789,0.0006258526,0.001438689,0.0034394,0.001748727,0.002507452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327284,"about_ca_system_score_gemma":0.00714427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01919861,"about_ca_topic_score_gemma":0.0239698,"domain_scores_codex":[0.9971766,0.0009886754,0.0002961507,0.0006472395,0.0007463419,0.0001450294],"domain_scores_gemma":[0.9867747,0.009277185,0.0008571316,0.001406349,0.001350766,0.0003338376],"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":[0.001708504,0.0005617972,0.1858638,0.002921655,0.002139174,0.0006690921,0.003541293,0.07193279,0.003823453,0.03563028,0.2249528,0.4662554],"study_design_scores_gemma":[0.000394519,0.0002096284,0.05651029,0.000664195,0.0004296628,0.0006862225,0.001396154,0.7863609,0.006447561,0.06305388,0.08349259,0.0003543586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08515003,0.0003272381,0.7891541,0.002547215,0.0002463632,0.002412181,0.07257164,0.03970604,0.00788522],"genre_scores_gemma":[0.1068395,0.0001506023,0.8614994,0.0002571086,0.00004844949,0.00262218,0.02546982,0.001420595,0.001692336],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01919861,"threshold_uncertainty_score":0.05242985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1517890825353758,"score_gpt":0.4286937438238665,"score_spread":0.2769046612884907,"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."}}