This paper describes a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinical events that evolve over time. The methodology consists of three steps: (1) the incorporation of patient observations into a generic physiologic model, (2) the conversion of model states and predictions into domain-specific temporal abstractions, and (3) the transformation of temporal abstractions into clinically meaningful descriptive text. The first step converts raw observations to underlying model concepts, the second step identifies temporal features of the fitted model that have clinical interest, and the third step replaces features represented by model parameters and predictions into concepts expressed in clinical language. We describe a program, called TOPAZ, that uses this three-step methodology. TOPAZ generates a narrative summary of the temporal events found in the electronic medical record of patients receiving cancer chemotherapy. A unique feature of TOPAZ is its use of numeric and symbolic techniques to perform different temporal reasoning tasks. Time is represented both as a continuous process and as a set of temporal intervals. These two temporal models differ in the temporal ontology they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented.