Automatic Retrieval of Bone Fracture Knowledge Using Natural Language Processing JOURNAL OF DIGITAL IMAGING Do, B. H., Wu, A. S., Maley, J., Biswal, S. 2013; 26 (4): 709-713


Natural language processing (NLP) techniques to extract data from unstructured text into formal computer representations are valuable for creating robust, scalable methods to mine data in medical documents and radiology reports. As voice recognition (VR) becomes more prevalent in radiology practice, there is opportunity for implementing NLP in real time for decision-support applications such as context-aware information retrieval. For example, as the radiologist dictates a report, an NLP algorithm can extract concepts from the text and retrieve relevant classification or diagnosis criteria or calculate disease probability. NLP can work in parallel with VR to potentially facilitate evidence-based reporting (for example, automatically retrieving the Bosniak classification when the radiologist describes a kidney cyst). For these reasons, we developed and validated an NLP system which extracts fracture and anatomy concepts from unstructured text and retrieves relevant bone fracture knowledge. We implement our NLP in an HTML5 web application to demonstrate a proof-of-concept feedback NLP system which retrieves bone fracture knowledge in real time.

View details for DOI 10.1007/s10278-012-9531-1

View details for Web of Science ID 000322434700016

View details for PubMedID 23053906