Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death. Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient's deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient's risk of death. To help clinicians avoid remedies that may potentially contribute to a patient's death, researchers at MIT and elsewhere have developed a machine-learning model that could be used to identify treatments that pose a higher risk than other options.

Sepsis is a potentially life-threatening condition, occurs when your body has an unusually severe response to an infection. Some sepsis treatments lead to a patient's deterioration. Hence, selecting the optimal therapy is a challenging task. In recent years, Machine learning has successfully framed many sequential decision-making problems. Scientists at MIT and elsewhere do the same.