{"id":"W3116361237","doi":"10.1016/j.compbiomed.2020.104182","title":"Identifying subpopulations of septic patients: A temporal data-driven approach","year":2020,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"DBSCAN; Cluster analysis; Hierarchical clustering; Computer science; Sepsis; Intensive care unit; Data mining; Silhouette; Metric (unit); Medicine; Intensive care medicine; Artificial intelligence; Internal medicine; Engineering; Correlation clustering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002374603,0.0005539508,0.00107404,0.002736505,0.0004833058,0.001431371,0.001346034,0.0009645545,0.001148029],"category_scores_gemma":[0.007764624,0.0004298425,0.001376202,0.001841705,0.0002390446,0.001049569,0.001228231,0.001016555,0.0002380866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00062341,"about_ca_system_score_gemma":0.001279683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007720068,"about_ca_topic_score_gemma":0.01094782,"domain_scores_codex":[0.999188,0.0002407314,0.0001052461,0.0002636683,0.0001251187,0.00007726664],"domain_scores_gemma":[0.9967225,0.001981681,0.0003916807,0.0002459571,0.000452666,0.0002054756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001427792,0.0008524689,0.6763464,0.0003555266,0.0009630391,0.001379892,0.000539158,0.1753783,0.006727278,0.006393029,0.00614556,0.1234916],"study_design_scores_gemma":[0.00006523137,0.0002392622,0.05696234,0.00005549848,0.0002312112,0.0006055161,0.0006869414,0.9188139,0.001566911,0.01839788,0.00232465,0.00005062146],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6478707,0.001048511,0.3249087,0.002754272,0.0001535009,0.0005042692,0.0205088,0.000664162,0.001587011],"genre_scores_gemma":[0.9349731,0.0003028183,0.05398006,0.0001859572,0.00008937992,0.0002540936,0.00968703,0.00003692763,0.0004906978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007720068,"threshold_uncertainty_score":0.01535022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0506968495238665,"score_gpt":0.3129302391844497,"score_spread":0.2622333896605832,"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."}}