Journal article
Automatic Retrieval of Bone Fracture Knowledge Using Natural Language Processing
Journal of digital imaging, Vol.26(4), pp.709-713
08/2013
DOI: 10.1007/s10278-012-9531-1
PMCID: PMC3705014
PMID: 23053906
Abstract
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.
Details
- Title: Subtitle
- Automatic Retrieval of Bone Fracture Knowledge Using Natural Language Processing
- Creators
- Bao H Do - Division of Musculoskeletal Section, Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, S-056, Stanford, CA 94305 USAAndrew S Wu - Department of Radiology, Kaiser Permanente Downey Medical Center, 9333 Imperial Hwy, Downey, CA 90242 USAJoan Maley - Division of Neuroradiology, Department of Radiology, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242 USASandip Biswal - Division of Musculoskeletal Section, Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, S-056, Stanford, CA 94305 USA
- Resource Type
- Journal article
- Publication Details
- Journal of digital imaging, Vol.26(4), pp.709-713
- Publisher
- Springer US; Boston
- DOI
- 10.1007/s10278-012-9531-1
- PMID
- 23053906
- PMCID
- PMC3705014
- ISSN
- 0897-1889
- eISSN
- 1618-727X
- Language
- English
- Date published
- 08/2013
- Academic Unit
- Radiology; Otolaryngology
- Record Identifier
- 9984051976502771
Metrics
32 Record Views