Heart failure is often diagnosed too late and has a high death rate—over 50% of patients die within five years of diagnosis. Currently, it’s detected using a finger-prick blood test, but this test usually only shows results when the disease has already progressed. Not all patients get this test early enough. To help catch heart failure earlier, researchers are using deep learning to analyze patients’ medical records from GPs and hospitals. These records hold a timeline of a patient’s health events, and the order of these events is important. By spotting patterns in this data, the goal is to find people who are at risk and recommend early testing before symptoms become serious.