{"id":"W2523834880","doi":"10.2196/medinform.5909","title":"Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach","year":2016,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":533,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Sepsis; Medicine; Systemic inflammatory response syndrome; Mews; Intensive care unit; Early warning score; Machine learning; Intensive care medicine; Electronic health record; Intensive care; Artificial intelligence; Medical record; Computer science; Emergency medicine; Health care; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09192372534928704,"score_gpt":0.3520840859675537,"score_spread":0.2601603606182666,"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."}}