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Up to now, scholars have proposed some algorithms for learning synonyms of laboratory indicators. However, there is no report on learning multiple synonym sets at same time for outliers of laboratory indicators. Among the outliers of laboratory indicators of diseases, there are multiple synonym sets with various semantics such as {high, higher, increase}, {low, lower, decrease}, {very low, significantly low}. In this paper, a synonym set learning algorithm for different semantics of outliers of laboratory indicators is proposed. Firstly, using the sentence pattern laboratory indicators, outliers, and disease entities from medical texts are extracted; secondly, a set of synonyms for different semantics of laboratory indicators and outliers are learned; thirdly, the correspondences relation of laboratory indicators, outliers, and diseases are established. In the process of the first task, the algorithm is designed like a BERT model, that is, masking any one component and learning it with the other unmasked three parts, then updating the component sets that will include the learned components, and then with the new three unmasked components that includes the updated component sets to learn a new component that is included in the previous unmasked components. These steps run iteratively until every component sets do not varied. The experimental results show that the proposed algorithms is effective.