Harnessing Unstructured Data in Radiology

The harnessing of unstructured data is vital to moving the field of radiology forward. There are methods used for the mining of unstructured data, with one of the most common being Natural Language Processing (NLP). However, there are some difficulties with the use of NLP in the radiology field, because NLP lacks the capacity to analyze free-text radiology reports and images. There is too much uncertainty to be addressed with NLP, but there may be ways in which it can be useful. In order to make that determination, this paper examines the current usage of NLP and other methods such as RadLex and Annotation and Image Markup for unstructured data mining in the radiology field, as well as the desired and sought out use of the mining of unstructured data. Both clinical decision support and research analysis could benefit from unstructured data mining in the...
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