Individualized sepsis treatment using reinforcement learning

Reinforcement learning is applied to two large databases of electronic health records for patients admitted to an intensive care unit to identify individualized treatment strategies for correcting hypotension in sepsis.

[1]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[2]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[3]  Wu Ji,et al.  Early versus delayed administration of norepinephrine in patients with septic shock , 2014, Critical Care.

[4]  E. Rivers,et al.  EARLY INTERVENTIONS IMPROVE OUTCOME , 2016 .

[5]  P. Marik,et al.  A rational approach to fluid therapy in sepsis. , 2016, British journal of anaesthesia.

[6]  Suchi Saria,et al.  A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves , 2016, ArXiv.

[7]  Shamim Nemati,et al.  Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  S. Nemati,et al.  Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach. , 2016, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[9]  Barbara E. Engelhardt,et al.  A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units , 2017, UAI.

[10]  Aldo A. Faisal,et al.  The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care , 2018, Nature Medicine.

[11]  Rinaldo Bellomo,et al.  Liberal Versus Restrictive Intravenous Fluid Therapy for Early Septic Shock: Rationale for a Randomized Trial. , 2018, Annals of emergency medicine.

[12]  Manu L. N. G. Malbrain,et al.  Principles of fluid management and stewardship in septic shock: it is time to consider the four D’s and the four phases of fluid therapy , 2018, Annals of Intensive Care.

[13]  Suchi Saria,et al.  Discretizing Logged Interaction Data Biases Learning for Decision-Making , 2018, ArXiv.