{"id":"W2890846596","doi":"10.5539/ijsp.v7n6p23","title":"Unsupervised Machine Learning for Co/Multimorbidity Analysis","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exploratory analysis; Cluster analysis; Population; Multimorbidity; Hierarchical clustering; Unsupervised learning; Computer science; Artificial intelligence; Machine learning; Medicine; Data science; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004769999,0.00007604549,0.0002131872,0.0001392831,0.00005001737,0.00005284674,0.00009234066,0.00002289103,0.0004833961],"category_scores_gemma":[0.0005368288,0.00006173776,0.00009654142,0.00008037047,0.0001470767,0.00006948677,0.00002836144,0.00009983103,0.000001760985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000784947,"about_ca_system_score_gemma":0.00009552587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003864645,"about_ca_topic_score_gemma":0.00003597528,"domain_scores_codex":[0.9991189,0.00003132111,0.0003381109,0.000117087,0.000306886,0.00008766534],"domain_scores_gemma":[0.998425,0.0001769822,0.0001995859,0.0000741194,0.001035862,0.00008845791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004740692,0.001177068,0.8545017,0.0005565871,0.01017014,0.0001417263,0.0007602245,0.0007852942,0.0005337805,0.04030433,0.005971076,0.08035742],"study_design_scores_gemma":[0.006327511,0.001867991,0.7558342,0.00009014528,0.002881613,0.00005954327,0.0001329004,0.1409018,0.0002865747,0.05223303,0.03914576,0.0002388568],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5894725,0.000186714,0.4056921,0.001510867,0.000451535,0.0003659567,0.0006030834,0.00001727497,0.001699938],"genre_scores_gemma":[0.9696743,0.00008815232,0.02960532,0.00009734571,0.0002807187,0.000002593027,0.0001269426,0.000005428362,0.0001191903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3802018,"threshold_uncertainty_score":0.5292848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04998952867179129,"score_gpt":0.3710274162771394,"score_spread":0.3210378876053481,"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."}}