PROJECT ABSTRACT Sepsis, a life-threatening organ dysfunction syndrome due to infection, is common in hospitalized patients and leads to significant morbidity, mortality, and costs. Over 1.7 million patients develop sepsis in the United States each year, a number that will increase as the population ages. Patients with sepsis contribute to over $24 billion in healthcare costs yearly, and a recent study found that sepsis contributed to up to half of hospital deaths. Furthermore, survivors of sepsis suffer long-term cognitive impairment and physical disability. Therefore, improving the care of patients with sepsis would be enormously beneficial to society. However, there are several critical gaps in the field that need to be addressed: 1) delays in identifying infected patients are common and associated with increased mortality; 2) errors in risk stratification of patients with impending critical illness and sepsis are common and deadly; 3) current treatment strategies for infected patients utilize a one-size-fits-all approach, which neglects the wide range of clinical presentations and underlying biology due to the complex interactions between patient characteristics, the infectious organism, and the host immune response. The overall vision of the PI’s research program is to address these knowledge gaps by utilizing detailed multicenter electronic health record (EHR), clinical trial, and biomarker data combined with machine learning approaches to improve the identification, risk stratification, and discover important subphenotypes of sepsis to decrease preventable death from infection. Over the past five years, the PI has successfully secured independent funding through an NIGMS R01 and Department of Defense award. The PI has published over 80 peer-reviewed publications during this time, is an active member on several national and international committees, has participated in several NIH study sections, and has 40 mentees, including six with NIH K-level awards. Importantly, the PI has also developed and implemented a machine learning risk stratification tool, called eCART, in over 20 hospitals, which has decreased mortality in high-risk ward patients. The goal of the next five years is to build upon these successes and address key gaps in the field through three future directions: 1) using natural language processing and deep learning to improve the identification and risk stratification of infected patients, 2) identifying important subphenotypes using research biomarkers, and 3) using machine learning to develop personalized treatment algorithms. These projects are innovative because they will utilize advanced machine learning methods in a large, multicenter collection of structured and unstructured EHR and biomarker data for developing novel tools in patients with sepsis. In the future, these models will be implemented for earlier identification, accurate risk stratification, and to deliver personalized care at the bedside. This has the potenti...