Assessment of patient eligibility is an essential process in the clinical trial but there are a lot of manual processes involved. Natural Language Processing (NLP) is a promising technique to automate analysing of the massive volume of Electronic Medical Records (EMRs) hence it can assist in the assessment of patient eligibility, especially in clinical trials that require complex inclusion/exclusion criteria. In this paper, we proposed a hybrid model which utilized both rule-based and NLP technologies to automate the assessment of patient eligibility. The result showed that the hybrid model had a better trade-off between sensitivity and precision compared to the rule-based model and NLP similarity model. Moreover, the accuracy of the hybrid model was validated on the larger dataset and it reached an accuracy of 87.3%. Therefore, this technique potentially can improve the efficiency of patient recruitment by eliminating the manual processes that involve in the assessment of patient eligibility.