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

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.

The opportunities and shortcomings of using big data and national databases for sarcoma research

“…Although some view this as a benefit because it allows observation and monitoring of the real-world patterns of oncologic treatment patterns and management, it can also lead to poor data quality because of the heterogeneous nature of EHR utilization, differences in workflows, or incomplete recording of data in discrete fields. 47 Moreover, data standards for oncology have not been widely adopted, and despite attempts to compensate for widespread differences in data models and ontologies across specialties, it is still difficult to collect complete and uniform data from many institutions. 46 Unstructured data captured in EHRs remain a major limitation of RWE; data existing outside structured or discrete fields are difficult to capture and require manual chart abstraction by experienced curators.…”

“…Natural language processing solutions are showing promise for helping to automate the conversion of free-text data to structured data for research. 47 Moreover, data standards for oncology have not been widely adopted, and despite attempts to compensate for widespread differences in data models and ontologies across specialties, it is still difficult to collect complete and uniform data from many institutions. ASCO recently issued a position statement calling for legislation to mandate interoperability of EHRs among hospital systems and oncology practices.…”

Infrastructure of the Electronic Health Record Data Management for Digital Patient Phenotype Creating

“…In addition, J. Luo, M. Wu, et al in their review [11] draw attention to the fact that the application of artificial intelligence technologies and Big Data to solve health problems based on EHC has its own specifics and differs from such areas biomedical informatics, as computer modeling of organs, image processing and analysis, genome research. In this context, the researchers such as L. Luo, L. Li, J. Hu, et al [12]; K. Kreimeyer, M. Foster, A. Pandey, et al [13]; A. Névéol, H. Dalianis, S. Velupillai, et al [14] highlight the problem of extracting objective information from medical texts included in EHR. They note the objectivity of the fact that there are many natural language processing systems (NLP) for English medical texts processing.…”

Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis

“…Big data analysis has opened the door to a new era in biomedical fields, such as healthcare [1] and disease diagnosis [2,3], etc. Abbreviations are appearing more and more frequently in these areas, which significantly hinders development in related research fields such as biomedical text analysis [4,5], large biomedical ontologies [6]. Abbreviations are used in almost all types of data (structured, semi-structured, unstructured).…”