Anticipating heart failure with machine learning.
Every year, roughly one out of eight U.S. deaths is caused at least in part by heart failure. One of acute heart failure’s most common warning signs is excess fluid in the lungs, a condition known as pulmonary edema.
A patient’s exact level of excess fluid often dictates the doctor’s course of action, but making such determinations is difficult and requires clinicians to rely on subtle features in X-rays that sometimes lead to inconsistent diagnoses and treatment plans.
To better handle that kind of nuance, a group led by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can look at an X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 (healthy) to 3 (very, very bad). The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.
Working with Beth Israel Deaconess Medical Center (BIDMC) and Philips, the team plans to integrate the model into BIDMC’s emergency-room workflow this fall.
