{"id":"W3128825412","doi":"10.1186/s13040-021-00235-0","title":"Data analytics and clinical feature ranking of medical records of patients with sepsis","year":2021,"lang":"en","type":"article","venue":"BioData Mining","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Krembil Foundation","funders":"University of Toronto","keywords":"Sepsis; Medical record; Septic shock; Medicine; Context (archaeology); Ranking (information retrieval); Electronic medical record; Machine learning; Binary classification; Computer science; Logistic regression; SOFA score; Artificial intelligence; Intensive care medicine; Data mining; Emergency medicine; Internal medicine; Support vector machine","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":[0.003860968,0.0007202197,0.0007397787,0.006894547,0.0004715568,0.001538791,0.0007600565,0.00083233,0.0007429594],"category_scores_gemma":[0.02415364,0.0001682403,0.0009807163,0.006003263,0.000361078,0.001221218,0.000757811,0.0008950296,0.0004694907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200505,"about_ca_system_score_gemma":0.001344352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006069992,"about_ca_topic_score_gemma":0.006051533,"domain_scores_codex":[0.9952058,0.001634012,0.0008329981,0.0008171857,0.001181214,0.0003288465],"domain_scores_gemma":[0.9786972,0.01258335,0.00362063,0.00154002,0.003019702,0.0005391329],"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":[0.0008040715,0.000716246,0.835912,0.0005417802,0.0004204907,0.0004236248,0.0002679864,0.02173102,0.002042151,0.0008012626,0.007942503,0.1283969],"study_design_scores_gemma":[0.00008355798,0.0007374308,0.6946102,0.0002520751,0.0002260823,0.001045475,0.001267796,0.2847658,0.005602969,0.003548408,0.007770318,0.00008991408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9415246,0.002500246,0.01636924,0.002237899,0.0001983041,0.0002862198,0.03477471,0.0007163283,0.001392496],"genre_scores_gemma":[0.9510968,0.0003392745,0.0166043,0.0001329847,0.0001162597,0.0001342883,0.03138074,0.00001306634,0.0001823042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006894547,"threshold_uncertainty_score":0.020419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2182863075406786,"score_gpt":0.4170764311393291,"score_spread":0.1987901235986505,"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."}}