Assertion information is of crucial importance for constructing an intelligent diagnosis system as it contains clinical findings and decision basis of clinicians in the electronic medical records (EMRs), e.g., whether a symptom is present or not. Current work mainly treats assertion detection as a sequence labeling task, or constructs rule-based methods. However, there is a challenge that to detect assertions embedded in the context with long-range dependencies, needs considering the whole text to capture the complex linguistic and underlying semantic information. To tackle the above issues, we consider assertion detection as a semantic reasoning task based on natural language inference (NLI). First, we generate candidate spans with boundary detection on the basis of which we can enrich the training corpus with external knowledge such as assertion definitions. Then we detect the assertions through the NLI-based classification. To the best of our knowledge, we build the first Chinese assertion dataset, which contains 4237 sentences on privacy de-identified ophthalmology remote reading reports. Extensive experiments demonstrate that our proposed method achieves the best results on both of the English dataset i2b2 and the Chinese privacy dataset.