Medical knowledge extraction has great potential to improve the treatment quality of hospitals. In this paper, we propose a clinical problem-action relation extraction method. It is based on clinical semantic units and event causality patterns in order to present a chronological view of a patient's problem and a physician's action. Based on our observation, a clinical semantic unit is defined as a conceptual medical knowledge for a problem and/or action. Since a clinical event is a basic concept of the problemaction relation, events are detected from clinical texts based on conditional random fields. A clinical semantic unit is segmented from a sentence based on time expressions and inherent structure of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on event causality features in support vector machines. The experimental result on Korean medical collection shows 78.8% in F-measure when given the answer of clinical events. This result shows that the proposed method is effective for extracting clinical problem-action relations.