Unsupervised machine learning identifies symptoms of indigestion as a predictor of acute decompensation and adverse cardiac events in patients with heart failure presenting to the emergency department

More than six million people in the United States have heart failure (HF).1 There are approximately one million incident cases diagnosed annually, and it is projected that the prevalence of HF will increase by 46% by 2030.2 Despite progressive advances in guideline-directed medical therapies, survival rates have leveled off over time, with the current 5-year mortality rate remaining as high as 42.3% −52.6%.3,4 In fact, HF is listed as a cause of death in approximately 13% of death certificates nationwide.
Source: Heart and Lung - Category: Intensive Care Authors: Source Type: research