Sepsis: a roadmap for future research.

By Jonathan Cohen, Jean-Louis Vincent, N. Adhikari, F. Machado, Derek C. Angus, Thierry Calandra, K. Jaton, S. Giulieri, J. Delaloye, S. Opal and 3 more. 2015

942 citations recorded by the source corpus.

Subfields: Critical Care and Intensive Care Medicine, Epidemiology, Pulmonary and Respiratory Medicine

How researchers see this paper

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

In Critical Care and Intensive Care Medicine

1. Mitchell M. Levy
2. Leo Anthony Celi
3. Derek C. Angus
4. Marzyeh Ghassemi
5. Theodore J. Iwashyna

In Epidemiology

1. Matthieu Komorowski
2. Marzyeh Ghassemi
3. Leo Anthony Celi
4. Aniruddh Raghu
5. Peter Szolovits
6. Mitchell M. Levy
7. Derek C. Angus
8. A. Rhodes

In Pulmonary and Respiratory Medicine

1. Leo Anthony Celi
2. Marzyeh Ghassemi
3. Theodore J. Iwashyna

Citations in the corpus

• Deep Reinforcement Learning for Sepsis Treatment (2017, background): “Sepsis (severe infections with organ failure) is a dangerous condition that costs hospitals billions of pounds in the UK alone [15], and is a leading cause of patient mortality [1].”
• Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach (2017, background): “Sepsis (severe infections with organ failure) is a dangerous condition that costs hospitals billions of pounds in the UK alone (Vincent et al., 2006), and is a leading cause of patient mortality (Co-hen et al., 2006).”
• Sepsis and septic shock. (2018, background): “Thrombomodulin, another drug that modulates the coagulation pathway, was shown to be safe in human beings, 130 and its clinical efficacy is being studied in an RCT. 129 Immunomodulation and mesenchymal stem cells are two of the latest developments in terms of possible therapeutic interventions.”
• Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. (2016, background): “(6) Sepsis is also not tracked in the Global Burden of Disease taxonomy.”
• The Timing of Early Antibiotics and Hospital Mortality in Sepsis (2017, background): “It is widely accepted and biologically plausible that giving antibiotics as early as possible to sepsis patients should improve their outcomes.(1, 2) This has motivated both international guidelines as well as quality benchmarks in sepsis care.”
• Model-Based Reinforcement Learning for Sepsis Treatment (2018, background): “Sepsis (severe infections with organ failure) is a dangerous condition that is a leading causing of patient mortality and is expensive for hospitals to treat [1, 20].”