Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?

Abstract

Department of Computer Science, Johns Hopkins University; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health; and Machine Learning, Artificial Intelligence, and Health Care Lab, Johns Hopkins University, Baltimore, MD Department of Computer Science, Johns Hopkins University, Baltimore, MD Dr. Saria has grants from Gordon and Betty Moore Foundation, the National Science Foundation, the National Institutes of Health, Defense Advanced Research Projects Agency, and the American Heart Association; she is a founder of and holds equity in Bayesian Health; she is the scientific advisory board member for PatientPing; and she has received honoraria for talks from a number of biotechnology, research, and healthtech companies. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. Ms. Henry is entitled to royalties from a licensing agreement between Johns Hopkins University and Bayesian Health LLC. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. For information regarding this article, E-mail: [email protected]

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