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Large collections of electronic patient records provide abundant but under-explored information on the real-world use of medicines. Although they are maintained for patient administration, they provide a broad range of clinical information for data analysis. One growing interest is drug safety signal detection from these longitudinal observational data. In this paper, we proposed two novel algorithms-a likelihood ratio model and a Bayesian network model-for adverse drug effect discovery. Although the performance of these two algorithms is comparable to the state-of-the-art algorithm, Bayesian confidence propagation neural network, the combination of three works better due to their diversity in solutions. Since the actual adverse drug effects on a given dataset cannot be absolutely determined, we make use of the simulated observational medical outcomes partnership (OMOP) dataset constructed with the predefined adverse drug effects to evaluate our methods. Experimental results show the usefulness of the proposed pattern discovery method on the simulated OMOP dataset by improving the standard baseline algorithm-chi-square-by 23.83%.