{"id":"W4414531239","doi":"10.1186/s13040-025-00480-7","title":"Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering","year":2025,"lang":"en","type":"article","venue":"BioData Mining","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Multivariate statistics; Sepsis; Covariate; Silhouette; Disease; Biobank; Multivariate analysis; Metric (unit)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00256132,0.0005546821,0.0006200995,0.001632908,0.0004619719,0.0007556882,0.0006678177,0.0005402258,0.0006441487],"category_scores_gemma":[0.006138397,0.0002224755,0.0008220017,0.001122355,0.0002282824,0.0004983079,0.0008932034,0.0007391527,0.0003210494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005530801,"about_ca_system_score_gemma":0.0009460476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007898129,"about_ca_topic_score_gemma":0.006272862,"domain_scores_codex":[0.9992473,0.0003297778,0.00005904696,0.0001867906,0.00009123098,0.00008584542],"domain_scores_gemma":[0.997811,0.0007549901,0.000465429,0.0003220681,0.0004594142,0.0001871484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001956486,0.0004347763,0.6724594,0.0001592169,0.0006392748,0.0004512558,0.0007594678,0.1592292,0.007390224,0.002462703,0.005472742,0.1485851],"study_design_scores_gemma":[0.0000379194,0.0002105377,0.083908,0.00004953392,0.0001644686,0.0002663279,0.0003071054,0.907019,0.002124066,0.004121993,0.001720292,0.00007073064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8180128,0.001537145,0.1761593,0.0008930718,0.0001017874,0.0001370962,0.001612761,0.0005501703,0.0009957852],"genre_scores_gemma":[0.96808,0.0003297436,0.02916596,0.00006747918,0.0000550499,0.0000694931,0.001736856,0.00003443933,0.0004609162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007898129,"threshold_uncertainty_score":0.01570427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0254430725212012,"score_gpt":0.2456228539031261,"score_spread":0.2201797813819249,"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."}}