CRTS: A type system for representing clinical recommendations
September 06, 2016 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Ravi P Garg, Kalpana Raja, Siddhartha R Jonnalagadda
arXiv ID
1609.01592
Category
cs.CL: Computation & Language
Cross-listed
cs.CY
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Background: Clinical guidelines and recommendations are the driving wheels of the evidence-based medicine (EBM) paradigm, but these are available primarily as unstructured text and are generally highly heterogeneous in nature. This significantly reduces the dissemination and automatic application of these recommendations at the point of care. A comprehensive structured representation of these recommendations is highly beneficial in this regard. Objective: The objective of this paper to present Clinical Recommendation Type System (CRTS), a common type system that can effectively represent a clinical recommendation in a structured form. Methods: CRTS is built by analyzing 125 recommendations and 195 research articles corresponding to 6 different diseases available from UpToDate, a publicly available clinical knowledge system, and from the National Guideline Clearinghouse, a public resource for evidence-based clinical practice guidelines. Results: We show that CRTS not only covers the recommendations but also is flexible to be extended to represent information from primary literature. We also describe how our developed type system can be applied for clinical decision support, medical knowledge summarization, and citation retrieval. Conclusion: We showed that our proposed type system is precise and comprehensive in representing a large sample of recommendations available for various disorders. CRTS can now be used to build interoperable information extraction systems that automatically extract clinical recommendations and related data elements from clinical evidence resources, guidelines, systematic reviews and primary publications. Keywords: guidelines and recommendations, type system, clinical decision support, evidence-based medicine, information storage and retrieval
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