WebMD features Dr. Suchi Saria in a discussion on how artificial intelligence works, the benefits of using AI in healthcare, and the role it will play (and won’t play) in the future of healthcare delivery.
See the full video here.
Popular Science named Dr. Suchi Saria one of the 10 most brilliant people in 2016, based on her work applying machine learning to improve sepsis care by helping providers find and treat sepsis early.
Read the full profile here.
IEEE Spectrum quoted Dr. Suchi Saria in an article discussing artificial intelligence’s role in clinical decision support among COVID patients.
Read the full article here.
PBS discusses how an early version of Bayesian’s platform was able to detect sepsis early, guide providers to intervene and save lives.
Read the full article here.
Bloomberg Businessweek quoted Dr. Suchi Saria in an article discussing how machine learning can be used to analyze complex data, recognize warning signs, and predict fatal conditions in hospitalized patients.
Read the full article here.
Dr. Suchi Saria discusses the development and outcomes of her early sepsis warning system, TREWS, and a call to action for industry experts and policymakers in this TEDx talk.
Watch the TedTalk here.
Dr. Suchi Saria shares her journey bringing machine learning to the bedside, overcoming barriers, and creating practical applications that impact real patients.
Watch the TedMed talk here.
Dr. Suchi Saria joins a panel of fellow industry experts to discuss artificial intelligence as the single biggest opportunity in healthcare today, and it’s capacity to shift reactive care to proactive care.
See the full panel here.
Dr. Suchi Saria discusses her journey working with a multidisciplinary team to successfully deploy artificial intelligence at the bedside, create actionable alerts, and improve patient outcomes.
See the full workshop here.
A targeted real-time early warning score (TREWScore) for septic shock
Sepsis is a leading cause of death, contributing to 1 in every 2 or 3 hospital deaths. Early identification and treatment has a dramatic impact on morbidity and mortality. In a cover article published in Science Translational Medicine, Dr. Saria and colleagues showed that routinely available vital signs and lab results could be used to predict which patients would experience septic shock. TREWScore (Targeted Real-Time Early Warning Score) was more accurate than a routine screening protocol and another score used clinically for predicting septic shock (MEWS). TREWScore identified patients 28.2 hours (median) before onset with ⅔ of cases identified before any sepsis-related organ dysfunction.
This was the first study to show that applying machine learning techniques to clinical data could be used to proactively identify patients at risk of sepsis. Since then, over 200 related papers have been published.
Read the full research paper here.
