Deep learning in clinical natural language processing: a methodical review

S Wu, K Roberts, S Datta, J Du, Z Ji, Y Si… - Journal of the …, 2020 - academic.oup.com
Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

A systematic review of natural language processing applied to radiology reports

A Casey, E Davidson, M Poon, H Dong… - BMC medical informatics …, 2021 - Springer
Background Natural language processing (NLP) has a significant role in advancing
healthcare and has been found to be key in extracting structured information from radiology …

Enhancing clinical concept extraction with contextual embeddings

Y Si, J Wang, H Xu, K Roberts - Journal of the American Medical …, 2019 - academic.oup.com
Objective Neural network–based representations (“embeddings”) have dramatically
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …

Natural language processing for smart healthcare

B Zhou, G Yang, Z Shi, S Ma - IEEE Reviews in Biomedical …, 2022 - ieeexplore.ieee.org
Smart healthcare has achieved significant progress in recent years. Emerging artificial
intelligence (AI) technologies enable various smart applications across various healthcare …

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

K Adnan, R Akbar - International Journal of Engineering …, 2019 - journals.sagepub.com
During the recent era of big data, a huge volume of unstructured data are being produced in
various forms of audio, video, images, text, and animation. Effective use of these …

Systematic literature review of information extraction from textual data: recent methods, applications, trends, and challenges

MHA Abdullah, N Aziz, SJ Abdulkadir… - IEEE …, 2023 - ieeexplore.ieee.org
Information extraction (IE) is a challenging task, particularly when dealing with highly
heterogeneous data. State-of-the-art data mining technologies struggle to process …

GNER: A generative model for geological named entity recognition without labeled data using deep learning

Q Qiu, Z Xie, L Wu, L Tao - Earth and Space science, 2019 - Wiley Online Library
A variety of detailed data about geological topics and geoscience knowledge are buried in
the geoscience literature and rarely used. Named entity recognition (NER) provides both …

Unstructured text documents summarization with multi-stage clustering

MY Saeed, M Awais, R Talib, M Younas - IEEE Access, 2020 - ieeexplore.ieee.org
In natural language processing, text summarization is an important application used to
extract desired information by reducing large text. Existing studies use keyword-based …

Unsupervised approaches for textual semantic annotation, a survey

X Liao, Z Zhao - ACM Computing Surveys (CSUR), 2019 - dl.acm.org
Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has
long been a research topic among various communities. The most challenging problem in …

Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment

I Banerjee, K Li, M Seneviratne, M Ferrari, T Seto… - JAMIA …, 2019 - academic.oup.com
Background The population-based assessment of patient-centered outcomes (PCOs) has
been limited by the efficient and accurate collection of these data. Natural language …