An AI-driven alarm system helps rescue patients before infections become fatal.
The problem: Machine learning can spot patterns in electronic health data indicating where a patient’s condition is headed that may be too subtle for doctors and nurses to catch. Sepsis, for instance, is a response to infection that inflames a patient’s organs, killing some 270,000 Americans each year. The ability to catch it early can save lives.
The solution: Sepsis Watch is a deep learning model that spots signs of sepsis up to five hours before it becomes dangerous. This crucial window allows clinicians to intervene.
How it works: The system integrates vital signs, test results, and medical histories of emergency-room patients, assessing their risk of septic shock on a scale of 0 to 100 percent. If the risk reaches 60 percent, the system alerts nurses in the hospital’s rapid response team. It also publishes an hourly list of each patient’s septic risk score.
- Researchers from Duke University, Harvard, and Google trained the model on a dataset of 50,000 patient records from the Duke hospital system.
- They evaluated the model at Duke and later expanded to two other community hospitals. All three continue to use it.
- The researchers designed Sepsis Watch with input from the hospital’s rapid response nurses. The collaboration, they say, made staff more likely to use the app.
Status: Duke physician and data scientist Mark Sendak and colleagues conducted a clinical trial between November 2018 and July 2019. Sepsis Watch significantly improved sepsis response times, Sendak told The Batch. The team plans to publish the results in the near future. Last July, Duke University licensed the software to Cohere Med, an AI healthcare startup.
Behind the news: Suchi Saria, a machine learning expert at Johns Hopkins University, was a pioneer in the use of reinforcement learning to identify sepsis treatment strategies back in 2018. Duke’s Sendak helped evaluate models for other kinds of clinical decision support in a recent survey in the European Medical Journal. The authors’ picks included early-warning systems for cardiac arrest, surgical complications, pneumonia, and kidney disease.
Why it matters: As little as three hours of warning can give caregivers time to begin tests and medications that dramatically improve a sepsis victim’s odds of survival.
We’re thinking: Building a great model is Step 1. Deployment is Step 2. Collaborating with hospital staff is a sharp way to promote Step 3, utilization.
Learn how to build your own prognostic models in Course 2 of the AI For Medicine Specialization.