Constructing patient representation from EHRs has become an emerging hot research topic, as it is widely used for predicting disease prognosis, medication outcomes and mortality, and identifying patients who are similar to a target patient. Sequence alignment methods are able to preserve the temporal sequence information in patient medical records when constructing computable patient representation and thus are worth comprehensive and objective evaluation. In this work, we synthesized patient medical records using a set of synthesis operations on top of real patient medical records from a large real-world EHR database. Then we tested two cutting-edge sequence alignment methods, namely dynamic time warping (DTW) and Needleman-Wunsch algorithm (NWA) for the purpose of patient medical records alignment, in order to understand their strengths and limitations. Our results show that both DTW and NWA outperform the reference alignment. DTW seems to align better than NWA by inserting new daily events and identifying more similarities between patient medical records. By incorporating medical knowledge, we can improve the temporal sequence alignment by these algorithms even better and create more accurate patient representation for predictive models and patient similarity calculation.