A computer-implemented method for building predictive models for the estimation of adverse health outcome likelihood comprising: identifying a set of candidate model risk variables that are associated with an adverse outcome type, comprising selecting the candidate model risk variables using a reconciliation process between risk factors identified in outcome-specific etiological knowledge of the adverse outcome type and non-outcome patient training data; constructing an outcome likelihood model form that estimates the likelihood of the adverse outcome type using risk variables that are selected from the set of candidate model risk variables where the outcome likelihood model is computed based upon a model that predicts the likelihood of an adverse health outcome for a patient; classifying each of the selected risk variables into either a baseline group or a dynamic group, wherein: the baseline group is composed of those selected risk variables that are non-modifiable based on medical care that is provided to a patient; and the dynamic group is composed of those selected risk variables that are modifiable based on the medical care that is provided to the patient; constructing dynamic risk variable model forms that predict values for the selected risk variables in the dynamic group, the predicted values for the selected risk variables in the dynamic group corresponding to predictions of the selected risk variables in the dynamic group computed based on at least one of the risk variables in the baseline group; constructing a baseline outcome likelihood model form associated with the adverse outcome type using the outcome likelihood model form and the dynamic risk variable model forms; constructing a dynamic outcome likelihood model form associated with the adverse outcome type using the outcome likelihood model form and at least one of the selected risk variables; and fitting the constructed outcome likelihood model form, baseline outcome likelihood model form, and dynamic outcome likelihood model form, to a training data set which includes both outcome data and data values that correspond to the selected risk variables to produce an outcome likelihood model, a baseline outcome likelihood model, and a dynamic outcome likelihood model to be used as scoring functions to compute a baseline outcome likelihood and a dynamic outcome likelihood for patient data that is not represented in the training data set. identifying a set of candidate model risk variables that are associated with an adverse outcome type, comprising selecting the candidate model risk variables using a reconciliation process between risk factors identified in outcome-specific etiological knowledge of the adverse outcome type and non-outcome patient training data; constructing an outcome likelihood model form that estimates the likelihood of the adverse outcome type using risk variables that are selected from the set of candidate model risk variables where the outcome likelihood model is computed based upon a model that predicts the likelihood of an adverse health outcome for a patient; classifying each of the selected risk variables into either a baseline group or a dynamic group, wherein: the baseline group is composed of those selected risk variables that are non-modifiable based on medical care that is provided to a patient; and the dynamic group is composed of those selected risk variables that are modifiable based on the medical care that is provided to the patient; the baseline group is composed of those selected risk variables that are non-modifiable based on medical care that is provided to a patient; and the dynamic group is composed of those selected risk variables that are modifiable based on the medical care that is provided to the patient; constructing dynamic risk variable model forms that predict values for the selected risk variables in the dynamic group, the predicted values for the selected risk variables in the dynamic group corresponding to predictions of the selected risk variables in the dynamic group computed based on at least one of the risk variables in the baseline group; constructing a baseline outcome likelihood model form associated with the adverse outcome type using the outcome likelihood model form and the dynamic risk variable model forms; constructing a dynamic outcome likelihood model form associated with the adverse outcome type using the outcome likelihood model form and at least one of the selected risk variables; and fitting the constructed outcome likelihood model form, baseline outcome likelihood model form, and dynamic outcome likelihood model form, to a training data set which includes both outcome data and data values that correspond to the selected risk variables to produce an outcome likelihood model, a baseline outcome likelihood model, and a dynamic outcome likelihood model to be used as scoring functions to compute a baseline outcome likelihood and a dynamic outcome likelihood for patient data that is not represented in the training data set.