When it comes to the harnessing of unstructured data in radiology, it is very important to consider how much value that data will provide. In many cases, there is information in that data that can be valuable to the case and the patient, but only if the data is located and used correctly. Using Natural Language Processing (NLP) can help collect and process unstructured data from radiology reports, but there are difficulties with the accuracy of NLP in many cases, and that poses a big concern from a patient safety standpoint.
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 field of radiology, but only if the data can be mined correctly and the value can be extracted from it. With that in mind, various forms and methods used for the mining of unstructured data in radiology reports must be carefully considered and compared to one another, in order to find the method or combination of methods that works best and provides the most success for translation of unstructured data into valuable information for clinical decision support and research analysis.
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