{"id":"W2106981782","doi":"10.1145/2110363.2110408","title":"Unsupervised pattern discovery in electronic health care data using probabilistic clustering models","year":2012,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pacific Institute for the Mathematical Sciences","keywords":"Cluster analysis; Computer science; Probabilistic logic; Health records; Data mining; Unsupervised learning; Machine learning; Artificial intelligence; Cluster (spacecraft); Statistical model; Process (computing); Health care","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004447416,0.0001396085,0.0002279401,0.00009304897,0.0001133501,0.0002102857,0.0009540973,0.00003019853,0.000009522638],"category_scores_gemma":[0.00001069833,0.0001194851,0.00003944211,0.000378112,0.00001501784,0.003371218,0.001113531,0.0001328472,0.000003478968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002529088,"about_ca_system_score_gemma":0.0001787823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002297745,"about_ca_topic_score_gemma":0.0035221,"domain_scores_codex":[0.9982522,0.00007171703,0.0003227222,0.0004108266,0.0001880734,0.0007544181],"domain_scores_gemma":[0.9987859,0.00002753084,0.00008227345,0.0009865029,0.00002338159,0.00009438428],"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":[0.00002251314,0.0004094218,0.03304702,0.0006808171,0.0001230506,0.00001061325,0.02557355,0.4353041,0.0002736858,0.0440215,0.0001535186,0.4603802],"study_design_scores_gemma":[0.0001286396,0.00002512181,0.0003400478,0.00003441526,0.000004935657,0.000007123909,0.0003845995,0.9986431,0.000006638281,0.0001917896,0.0000840096,0.0001495988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07142358,0.00131687,0.9265258,0.0001583665,0.00008280144,0.0001508808,0.000006436775,0.00005492974,0.000280299],"genre_scores_gemma":[0.9863381,0.00002027595,0.01328093,0.0002204329,0.00007057715,0.0000031619,0.0000300147,0.00001170783,0.00002482571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9149145,"threshold_uncertainty_score":0.487246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07369182579551344,"score_gpt":0.2897209502123568,"score_spread":0.2160291244168434,"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."}}