{"id":"W4282962132","doi":"10.2196/37689","title":"Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Korea Institute of Science and Technology Information; Samsung; National Supercomputing Center, Korea Institute of Science and Technology Information; Korea Institute of Science and Technology; National Research Foundation","keywords":"Medicine; Sepsis; Receiver operating characteristic; Logistic regression; Cancer; Medical prescription; Machine learning; Medical record; Random forest; Emergency medicine; Intensive care medicine; Artificial intelligence; Internal medicine; Computer science","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":[0.0002775371,0.0001400207,0.0003575406,0.0001299462,0.0001985384,0.00002150198,0.0001278643,0.00004358339,0.0001964807],"category_scores_gemma":[0.00008324119,0.00008204366,0.00008257939,0.000403154,0.00007611411,0.0001042909,0.0001047134,0.0005847791,0.000001209178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004228721,"about_ca_system_score_gemma":0.0004398851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006503729,"about_ca_topic_score_gemma":0.00003752757,"domain_scores_codex":[0.9978617,0.00008635553,0.0005729112,0.00009892707,0.001142252,0.0002378357],"domain_scores_gemma":[0.9990351,0.0001119365,0.0004140259,0.0002124275,0.00007221878,0.0001542755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001839067,0.0007052174,0.9600158,0.0003926347,0.0001150027,0.00000283432,0.01323907,0.002073275,3.643793e-8,0.000002451397,0.0002639014,0.0230059],"study_design_scores_gemma":[0.02264567,0.003612375,0.6369814,0.001340374,0.000419055,0.00001907672,0.07279216,0.2531685,0.00004075353,0.000008709603,0.008451437,0.0005204817],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977081,0.0004877959,0.0002355976,0.0002578231,0.00006918669,0.0008159403,0.0001477444,0.00002330573,0.0002544933],"genre_scores_gemma":[0.9972625,0.00005685227,0.001019659,0.0009963242,0.000019074,0.0001978202,0.0004211619,0.00001708656,0.000009457965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3230344,"threshold_uncertainty_score":0.3345644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04534682778296099,"score_gpt":0.3433839276553197,"score_spread":0.2980370998723587,"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."}}