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

By Kory Kreimeyer, M. Foster, Abhishek Pandey, Nina Arya, Gwendolyn Halford, Sandra F. Jones, R. Forshee, Mark O Walderhaug, Taxiarchis Botsis. Journal of Biomedical Informatics, 2017

559 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6b2f329c08fb6ff7dd4da1a3386269b9f29474d7

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Citations in the corpus

The ‘Digital Twin’ to enable the vision of precision cardiology (2020, unknown): “Information is often contained in unstructured format, and its extraction requires either manual work or further research efforts of automation through natural language processing technologies.(79)”
How Machine Learning will Transform Biomedicine (2020, unknown): “…have attempted to use natural language processing algorithms to mine publications (Dong et al., 2018), EHRs (Shickel et al., 2018), and clinical reports (Kreimeyer et al., 2017; Pons et al., 2016) for useful knowledge, such as biomarker-therapy associations and biological pathways of interest.”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, background): “Similarly, Kreimeyer et al [117] review 86 papers focusing on clinical NLP systems and a set of 71 associated NLP tasks.”
Introducing Artificial Intelligence Training in Medical Education (2019, unknown): “Examples of advances in AI include natural language processing (NLP) [16], speech recognition [17], virtual agents [18], decision management [19], machine learning [20], deep learning [21], and robotic process automation [22].”
Two-stage Federated Phenotyping and Patient Representation Learning (2019, background): “Converting the unstructured data into useful structured representations will not only help data analysis but also improve efﬁciency in clinical practice (Jagan-nathan et al., 2009; Kreimeyer et al., 2017; Ford et al., 2016; Demner-Fushman et al., 2009; Murff et al., 2011; Friedman et al., 2004).”