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The design-science paradigm aims to push the limits of human and organisational capabilities by generating new and inventive artefacts. The design science research framework includes engineering that applies science through the design process to solve challenges. The design process looks for a solution to meet the environment's needs. On the other hand, a research study results from a collaboration between design and knowledge base to answer a research problem.
The three principal cycles in design science research are the relevance cycle between the environment and the design phase, the design cycle within the design phase, and the rigour cycle between the design phase and the knowledge base. While it is most well-known in the engineering and computer science disciplines, it is also applied in biomedical engineering and bioinformatics. It is relevant in biomedical engineering because of the connection between research outputs and the issue of scientific rigour. Three cases are presented in this article to depict design science study in biomedical and bioinformatics research. For each case discussed, machine learning has a big chance accompanied by unique challenges. In conclusion, the design science paradigm can also be used in biomedical engineering research to ensure that knowledge is relevant and appropriately included in the research process.
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