A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts
Source abstract
Abstract
Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Biomedical Text Mining and Ontologies · Advanced Text Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23284, USA
National Radiation Oncology Program, Department of Veteran Affairs, Richmond, VA 23249, USA
Funding
U.S. Department of Veterans Affairs
References (100+)
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