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Natural Language Processing for EHR-Based Computational Phenotyping

Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann et al. • IEEE Transactions on Computational Biology and Bioinformatics (2018)

This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categori...

OPENALEX Open Access DOI: https://doi.org/10.1109/tcbb.2018.2849968 PMC: 6388621

Natural Language Processing

Julia Hirschberg, Bruce W. Ballard, Donald M. Hindle • AT&T Technical Journal (1988)

Researchers in natural language processing (NLP) apply computational techniques to the understanding and generation of human language. Currently, we are focusing on the development of grammatical formalisms and parsing algorithms, appropria...

OPENALEX DOI: https://doi.org/10.1002/j.1538-7305.1988.tb00232.x

Ensembles machine learning for short duration rainfall intensity forecasting

Susilowati, Aprizal, Dyah Indriana Kusumastuti • Discover Artificial Intelligence (2026)

Abstract This study introduces an ensemble machine learning (EML) framework to enhance short-duration rainfall-intensity prediction under limited-data conditions. By integrating multiple base learners within a stacking architecture, the pro...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-01772-5

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

Rashmi Patel, Miguel E Rentería, Soon Nan Wee, Rajagopalan Ramaswamy et al. • BMJ Open (2022)

Purpose NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analy...

DOAJ Open Access (DOAJ) DOI: 10.1136/bmjopen-2021-057227

Empowering text classification with NLP and explainable AI for enhanced interpretability

Sumaya Mustafa, Mariwan Hama Saeed • Journal of Electrical Systems and Information Technology (2025)

Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address...

DOAJ Open Access (DOAJ) DOI: 10.1186/s43067-025-00273-2

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

Ivan S. Blekanov, Nikita Tarasov, Svetlana S. Bodrunova • Future Internet (2022)

Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the mo...

DOAJ Open Access (DOAJ) DOI: 10.3390/fi14030069

Natural Language Processing

Yasha Hasija, Rajkumar Chakraborty • CRC Press eBooks (2021)

Natural language processing is a fast-evolving field with popular applications in language translation, speech recognition, text classification, and summarization of text, among others. The central aspect of NLP is vectorizing text data. Te...

OPENALEX DOI: https://doi.org/10.1201/9781003090113-15-15

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

Jiaqi Fu, Chaixiu Li, Chunlan Zhou, Wenji Li et al. • Journal of Medical Internet Research (2023)

BackgroundGiven the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews ...

DOAJ Open Access (DOAJ) DOI: 10.2196/43349

Enhancing educational semantic retrieval through dual objective fine tuning of open source embedding models

Ramteja Sajja, Yusuf Sermet, Ibrahim Demir • Discover Education (2026)

Abstract Recent advances in AI have catalyzed the adoption of intelligent educational tools, yet many semantic retrieval systems remain ill-suited to the unique linguistic and structural characteristics of academic content. This study prese...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44217-026-01825-y

Adversarial Attacks on Deep-learning Models in Natural Language Processing

Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li • ACM Transactions on Intelligent Systems and Technology (2020)

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulne...

OPENALEX Open Access DOI: https://doi.org/10.1145/3374217

Recent Advances in Natural Language Processing

• Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory (2000)

This volume brings together revised versions of a selection of papers presented at the Second International Conference on “Recent Advances in Natural Language Processing” (RANLP’97) held in Tzigov Chark, Bulgaria, September 1997. The aim of...

OPENALEX DOI: https://doi.org/10.1075/cilt.189

Natural-language processing

Terry Patten, Paul S. Jacobs • IEEE Expert (1994)

Processing natural language such as English has always been one of the central research issues of artificial intelligence, both because of the key role language plays in human intelligence and because of the wealth of potential applications...

OPENALEX DOI: https://doi.org/10.1109/64.295134

Natural language processing (NLP) in management research: A literature review

Yue Kang, Zhao Cai, Chee‐Wee Tan, Qian Huang et al. • Journal of Management Analytics (2020)

Natural language processing (NLP) is gaining momentum in management research for its ability to automatically analyze and comprehend human language. Yet, despite its extensive application in management research, there is neither a comprehen...

OPENALEX Open Access DOI: https://doi.org/10.1080/23270012.2020.1756939