Knowledge of how diseases progress and transform is crucial for clinical decision making. Frequent pattern mining techniques, such as sequential pattern mining (SPM) algorithms, can automatically extract such knowledge from large collections of electronic medical records (EMR). However, EMR data are usually unorganized and highly noisy. Finding meaningful disease patterns often calls for manual manipulation such as cohort and feature selection on EMR data by medical professionals. In this paper, we propose a topic-model-based SPM approach to find disease progression patterns from diagnostic records. We improve the traditional SPM algorithms by filtering and grouping the diagnosis sequences according to different clinical topics. These topics represent certain clinical conditions with closely related diagnoses, and are detected without prior medical knowledge. The experiment on real-world EMR data shows that our approach is able to find meaningful progression patterns with less noises, and can help quickly identify interesting patterns related to a certain clinical condition with less human effort.