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. 2017 Sep:73:14-29.
doi: 10.1016/j.jbi.2017.07.012. Epub 2017 Jul 17.

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

Kory Kreimeyer  1 , Matthew Foster  2 , Abhishek Pandey  2 , Nina Arya  2 , Gwendolyn Halford  3 , Sandra F Jones  4 , Richard Forshee  2 , Mark Walderhaug  2 , Taxiarchis Botsis  2
Affiliations

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

Kory Kreimeyer et al. J Biomed Inform. 2017 Sep.

Abstract

We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

Keywords: Common data elements; Natural language processing; Review; Systematic.

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Figures

Fig. 1.
Fig. 1.
The coding results of the title and abstract screening following adjudication.
Fig. 2.
Fig. 2.
The coding results of the full text screening following adjudication.
Fig. 3.
Fig. 3.
The review process and the number of records in Phases 1–3.
Fig. 4.
Fig. 4.
The queried records and the percentage included after each screening phase by publication year. The number in each bar represents the total records found by the initial query that were published during that year. Note that 2016 is an incomplete year because the query was run on June 15 of that year. The two lines show the percentage of records published in each year that were included after review of their title and abstract (red dashed line) and full text (blue solid line). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 5.
Fig. 5.
The citation counts per year for the 10 publications with the most citations. Retrieved from Web of Science on 2016–11–03.
See this image and copyright information in PMC

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