Transferability and interpretability of the sepsis prediction models in the intensive care unit

Cecconi M(2018)Sepsis and septic shock Lancet 392 75-87

Evans L(2016)The third international consensus definitions for sepsis and septic shock (Sepsis-3) JAMA 315 801-810

Levy M(2001)Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care Crit Care Med 29 1303-1310

Rhodes A(2018)The changing paradigm of sepsis: early diagnosis, early antibiotics, early pressors, and early adjuvant treatment Crit Care Med 46 1690-1692

Singer M(2018)Presenting symptoms independently predict mortality in septic shock: Importance of a previously unmeasured confounder Crit Care Med 46 1592-1599

Deutschman CS(2015)A targeted real-time early warning score (TREWScore) for septic shock Sci Transl Med 7 299ra122-26

Seymour CW(2020)Early detection of sepsis utilizing deep learning on electronic health record event sequences Artif Intell Med 104 101820-84

Angus DC(2016)Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach JMIR Med Inform 4 e5909-34

Linde-Zwirble WT(2018)An interpretable machine learning model for accurate prediction of sepsis in the ICU Crit Care Med 46 547-47

Lidicker J(2019)Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping Mach Learn Healthc Conf PMLR 106 2-406

Cecconi M(2018)Sepsis and septic shock Lancet 392 75-87

Evans L(2016)The third international consensus definitions for sepsis and septic shock (Sepsis-3) JAMA 315 801-810

Levy M(2001)Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care Crit Care Med 29 1303-1310

Rhodes A(2018)The changing paradigm of sepsis: early diagnosis, early antibiotics, early pressors, and early adjuvant treatment Crit Care Med 46 1690-1692

Singer M(2018)Presenting symptoms independently predict mortality in septic shock: Importance of a previously unmeasured confounder Crit Care Med 46 1592-1599

Deutschman CS(2015)A targeted real-time early warning score (TREWScore) for septic shock Sci Transl Med 7 299ra122-26

Seymour CW(2020)Early detection of sepsis utilizing deep learning on electronic health record event sequences Artif Intell Med 104 101820-84

Angus DC(2016)Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach JMIR Med Inform 4 e5909-34

Linde-Zwirble WT(2018)An interpretable machine learning model for accurate prediction of sepsis in the ICU Crit Care Med 46 547-47

Lidicker J(2019)Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping Mach Learn Healthc Conf PMLR 106 2-406