The most advanced technologies and continuous innovations in the medical field require a necessary interaction between the clinical and the engineering world. In this context, software applications are proposed as a bridge between the two scientific fields and, therefore, as powerful tools, easy to use, and with great analytical skills. In this work, we propose CBRA as an innovative software platform, moving towards personalized medicine, which aims to simplify and speed up the triage of patients and support doctors in the diagnostic and prognostic phase.The computational core of the devised software application consists of a model-based identification algorithm, which enables the reconstruction of the cardiac biomarkers release curves in patients with ST-Elevation Acute Myocardial Infarction (STEMI). Identification and parametric optimization techniques allow the application of the proposed approach to each singular patient: based on a few experimental acquisitions, CBRA can extrapolate several quantitative features of high clinical relevance, thus facilitating and rendering more objective the clinical evaluation and therapeutic choices. A dedicated database to collect and manage patients clinical and personal data, as well as a graphical user interface, provides clinicians and researchers with an intuitive and user-friendly environment.In the following work, we present some examples of the possible applications of CBRA, ranging from the management of the cardiac biomarkers time-series, up to the real analysis of the clinical features that CBRA can extract from the reconstructed curve, such as, e.g., maximum concentration values of biomarkers in the plasma and relative times, in the distinct phases of the acute myocardial infarction, or identification of the time to onset of symptoms.CBRA makes it easy for clinicians to use modeling and parametric identification tools to reconstruct release curves. Furthermore, CBRA provides support to the clinical decision, thanks to its capability to extract information of high clinical relevance, not easily obtainable from the mere visual analysis of experimental samples. Having information about the previously listed clinical parameters could allow, e.g., identify in which stage of AMI the patient is, when She/He goes to the emergency room, with significant benefits in the therapy.