From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system

By Eren Gultepe, Jeffrey P. Green, Hien H. Nguyen, Jason Y. Adams, Timothy E. Albertson, Ilias Tagkopoulos. 2014

184 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Endocrinology, Diabetes and Metabolism, Epidemiology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Suchi Saria
2. Peter F. Schulam
3. Mihaela van der Schaar

In Epidemiology

1. Suchi Saria
2. David C. Kale
3. Randall C. Wetzel
4. Zachary Chase Lipton
5. Katherine A. Heller
6. Joseph D. Futoma
7. Sanjay Hariharan

Citations in the corpus

• A targeted real-time early warning score (TREWScore) for septic shock (2015, background): “For septic shock in particular, promising work includes that of predicting septic shock using high-fidelity physiological signals collected directly frombedsidemonitors (30, 31), inferring relationships between predictors of septic shock using Bayesian networks (32), and using routine measurements f”