New technologies in medicine have led to an explosion in the number of parameters that must be considered when diagnosing and treating a patient. Because of this high volume of data it is not possible for the human decision maker to take all information into account in arriving at a decision. Automated methods are needed to effectively evaluate electronic information in many formats and provide summaries to the medical professional. The task is complicated by the complexity of the data and the potential uncertainty of some of the results. In this article complexity and uncertainty in medical data are discussed in terms of both representation and types of analysis. Methods that can address multiple complex data types are illustrated and examples are provided for specific medical problems. These methods are particularly important for automated trend analysis in the personal health record as small errors can be propagated through the complex system resulting in incorrect diagnosis and treatment.