Critical care is provided to patients who require intensive monitoring and often the support of failing organs. Cardiovascular and circulatory diseases and dysfunctions are extremely common in this group of patients. However, cardiac disease states are highly patient-specific and every patient has a unique expression of the disease or underlying dysfunction. Clinical staff must consider many combinations of different disease scenarios based on frequently conflicting or confusing measured data on a patient’s condition. Successful diagnosis and treatment therefore often rely on the experience and intuition of clinical staff, increasing the likelihood for clinical errors. A cardiovascular (CVS) computerized model that uniquely represents the patient and underlying dysfunction or disease is developed. The CVS model is extended to account for the known physiologic mechanisms during spontaneous breathing and mechanical ventilation, thus increasing the model’s accuracy of representing a critically ill patient in the intensive care unit (ICU). The extended CVS model is validated by correctly simulating several well known circulatory mechanisms and interactions. An integral-based system parameter identification method is refined and extended to account for much smaller subsets of available input data, as usually seen in critical care units. For example, instead of requiring the continuous ventricle pressure and volume waveforms, only the end-systolic (ESV) and end-diastolic (EDV) volume values are needed, which can be even further reduced to only using the global end-diastolic volume (GEDV) and estimating the ventricle volumes. These changes make the CVS model and its application to monitoring more pplicable to a clinical environment. The CVS model and integral-based parameter identification approach are validated on data from porcine experiments of pulmonary embolism (PE), positive end-expiratory pressure (PEEP) titrations at different volemic levels, and 2 different studies of induced endotoxic (septic) shock. They are also validated on 3 adrenaline dosing data sets obtained from published studies in humans. Overall, these studies are used to show how the model and realistic clinical measurements may be used to provide a clear clinical picture in real-time. A wide range of clinically measured hemodynamics were successfully captured over time. The integral-based method identified all model parameters, typically with less than 10% error versus clinically measured pressure and volume signals. Moreover, patient-specific parameter relationships were formulated allowing the forward prediction of the patient’s response towards clinical interventions, such as administering a fluid bolus or changing the dose of an inotrope. Hence, the model and methods are able to provide diagnostic information and therapeutic decision support. In particular, tracking the model parameter changes over time can assist clinical staff in finding the right diagnosis, for example an increase in pulmonary vascular resistance indicates a developing constriction in the pulmonary artery caused by an embolus. Furthermore, using the predictive ability of the model and developed methods, different treatment choices and their effect on the patient can be simulated. Thus, the best individual treatment for each patient can be developed and chosen, and unnecessary or even harmful interventions avoided. This research thus increases confidence in the clinical applicability and validity of this overall diagnostic monitoring and therapy guidance approach. It accomplishes this goal using a novel physiological model of the heart and circulation. The integral-based parameter identification methods take dense, numerical data from diverse measurements and aggregate them into a clearer physiological picture of CVS status. Hence, the broader accomplishment of this thesis is the transformation, using computation and models, of diverse and often confusing measured data into a patient-specific physiological picture - a new model-based therapeutic.