| 摘要 |
A computer-implemented technique, including database processing, is used for identifying at risk exists in a claims database. The technique includes processing the patient information in the claims database to find and extract claims information for a group of depression patients. Next, using the extracted information, a set of events, relevant to depression, is defined. Next, the extracted information and set of events are processed to create event level information which is organized with respect to events rather than claims. A time window is defined for providing a timeframe from which to judge whether events should be considered in subsequent processing; and, a set of variables is defined as being potential predictors of adverse health outcomes. Subsequently, the event level information, using the time window and the set of variables, is processed to generate an analysis file. Statistical analysis, such as logistic regression, is performed on the analysis file to generate a prediction model where the prediction model is a function of a subset of the set of variables. Finally, the prediction model isand output at risk patients, diagnosed with depression, likely to have adverse health outcomes. <IMAGE> |
