Abstract: Extracting knowledge and insights from the data is the core of data science and informatics approach. In the medical field, big data analysis is applied to reveal causal relationships and enhance its repeatability and inter-pretability based on correlation mining. Analysis of data association with causality is of great significance for think tank research and intelligence perception. To reveal the causal relationship between knowledge, the paper introduces relevant data platforms and research progress, and proposes a medical knowledge mining ideas based on multi-space and deep data. One is the measurement and critical analysis of laboratory-clinical knowledge trans-formation; the other is scientific technological influence measurement; the third is the identification of cross-cutting and innovative frontiers; the last one is the mining of medical knowledge based on the combination of full-text, bib-liometrics and computational linguistics. The first three approaches expand the space of medical knowledge, includ-ing from basic research space to applied research space, and from scientific space to technological space. The fourth way deepens the disclosure and explanation of the causality of medical knowledge based on certainty or uncertainty of the medical knowledge.

Key words: big data linkage, biomedical knowledge discovery, non-patent literature, uncertainty argumentation mining, citation sentence analysis