A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts

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.

Privacy-preserving mimic models for clinical named entity recognition in French

“…In fact, clinical narrative text is complex, incorporating a large variety of medical terminologies, abbreviations, ambiguity, poor grammar and nested entities [18]. Nested entities are embedded entities contained in other entities.…”

Named Entity Recognition for Sheko Language Using Bidirectional LSTM

“…The rule-based approach has several disadvantages, including the need for a linguistic expert to identify all of the rules, the timeconsuming effort of establishing those rules, lack of portability, and high maintenance costs when new rules are added and old rules are removed. (7). The machine-learning approach uses NEs to extract some features from the prepared corpus and then learns based on those feature sets.…”

“…Machine learning is a simple, versatile, and easy-to-maintain method. (7) . Data were collected from three separate sources and preprocessed so that it could be fed into the BiLSTM model in a machine-readable format.…”

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

“…Textual descriptions in drug instructions usually take the form of long, complicated sentences, which are difficult to be processed by man or machine. Existing information extraction methods have been used for drug and disease entity recognition (Lin, & Xie, 2020;Sun et al, 2021;Zhu et al, 2021), drug-disease relationship extraction (Bose, et al, 2021;Fatehifar, & Karshenas, 2021;Mingliang, Jijun, & Fei, 2021), etc. Recently, pre-training models have become prominent in Natural Language Processing (NLP) tasks due to its generalization capability (Vaswani et al, 2017).…”