The existing tools for estimating the risk of death in patients after they experience acute coronary syndrome are commonly based on echocardiography and clinical risk scores (for example, the TIMI risk score). These identify a small group of high-risk patients who account for only a minority of the deaths that occur in patients after acute coronary syndrome. Here, we investigated the use of three computationally generated cardiac biomarkers for risk stratification in this population: morphologic variability (MV), symbolic mismatch (SM), and heart rate motifs (HRM). We derived these biomarkers from time-series analyses of continuous electrocardiographic data collected from patients in the TIMI-DISPERSE2 clinical trial through machine learning and data mining methods designed to extract information that is difficult to visualize directly in these data. We evaluated these biomarkers in a blinded, prespecified, and fully automated study on more than 4500 patients in the MERLIN-TIMI36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non-ST-Elevation Acute Coronary Syndrome-Thrombolysis in Myocardial Infarction 36) clinical trial. Our results showed a strong association between all three computationally generated cardiac biomarkers and cardiovascular death in the MERLIN-TIMI36 trial over a 2-year period after acute coronary syndrome. Moreover, the information in each of these biomarkers was independent of the information in the others and independent of the information provided by existing clinical risk scores, electrocardiographic metrics, and echocardiography. The addition of MV, SM, and HRM to existing metrics significantly improved model discrimination, as well as the precision and recall of prediction rules based on left ventricular ejection fraction. These biomarkers can be extracted from data that are routinely captured from patients with acute coronary syndrome and will allow for more accurate risk stratification and potentially for better patient treatment.