Clinical Information Extraction Applications: A Literature Review

By Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn and 1 more. Journal of Biomedical Informatics, 2017

856 citations recorded by the source corpus.

Background With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text. Objectives In this literature review, we present a review of recent published research on clinical information extraction (IE) applications. Methods A literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library. Results A total of 1,917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications (including disease areas, drug-related studies, and clinical workflow optimizations). Conclusions Clinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f84fc762a3229f436f2a472bd90ef5070b5d31fb

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Citations in the corpus

MSnet: A BERT-based Network for Gendered Pronoun Resolution (2019, background): “Coreference resolution is an essential field of natural language processing (Sukthanker et al., 2018) and has been widely used in many systems such as dialog system (Niraula et al., 2014; Wessel et al., 2017), relation extraction (Wang et al., 2018) and question answer (Vicedo and Ferrández, 2000).”
A Comparison of Word Embeddings for the Biomedical Natural Language Processing (2018, background): “There has been an increasing number of studies applying word embeddings in common NLP tasks, such as information extraction (IE) [4], [5], [6], information retrieval (IR) [7], sentiment analysis [8], [9], question answering [10], [11], and text summarization [12], [13].”
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (2020, background): “Many natural language processing systems have been developed to extract structured labels from free-text radiology reports (Pons et al., 2016; Yadav et al., 2016; Hassanpour et al., 2017; Annarumma et al., 2019; Savova et al., 2010; Wang et al., 2018; Chen et al., 2018; Bozkurt et al., 2019).”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, methodology): “Wang et al [53] proposed a system for the identification of congestive heart failure (CHF) from EHRs, which they prospectively validated.”
Undesirable biases in NLP: Averting a crisis of measurement (2022, background): “…transcribed and analyzed to improve service (e.g., Verma et al., 2011), millions of medical and legal texts are automatically searched to find relevant passages, at times supporting decisions that may literally be matters of life and death (e.g., Wang et al., 2018; Zhong et al., 2020).”
Large language models are few-shot clinical information extractors (2022, background): “Clinical text contains a large amount of valuable information that is not captured by the structured data fields in electronic health records (Zweigenbaum et al., 2007; Wang et al., 2018).”