Machine learning could aid sepsis care

CAMBRIDGE, Mass.—Late 2018 brought news that researchers from the Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) have developed a predictive model, using machine learning techniques and technology, that could guide clinicians in deciding when to give potentially life-saving drugs to patients being treated for sepsis in the emergency room.

In a paper presented at the American Medical Informatics Association’s Annual Symposium, the MIT and MGH researchers described a model that “learns” from health data on emergency-care sepsis patients and predicts whether a patient will need vasopressors within the next few hours. For the study, the researchers compiled the first-ever dataset of its kind for emergency room (ER) sepsis patients. In testing, the model could predict a need for a vasopressor more than 80 percent of the time.

Other models have been built to predict which patients are at risk for sepsis, or when to administer vasopressors, in intensive care units. But this is reportedly the first model trained on the task for the ER.

Next, the researchers aim to expand the work to produce more tools that predict, in real-time, if ER patients may initially be at risk for sepsis or septic shock.

In other high-tech diagnostic news from MIT early this year—albeit neither machine learning-oriented nor artificial intelligence-related—engineers have designed an ingestible, Jell-O-like pill that, upon reaching the stomach, quickly swells to the size of a soft, squishy ping-pong ball big enough to stay in the stomach for an extended period of time.

The inflatable pill is embedded with a sensor that continuously tracks the stomach’s temperature for up to 30 days. If the pill needs to be removed from the stomach, a patient can drink a solution of calcium that triggers the pill to quickly shrink to its original size and pass safely out of the body.

“The dream is to have a Jell-O-like smart pill that, once swallowed, stays in the stomach and monitors the patient’s health for a long time, such as a month,” says Xuanhe Zhao, associate professor of mechanical engineering at MIT.