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Journal articles on the topic 'Drug named entity recognition'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Korkontzelos, Ioannis, Dimitrios Piliouras, Andrew W. Dowsey, and Sophia Ananiadou. "Boosting drug named entity recognition using an aggregate classifier." Artificial Intelligence in Medicine 65, no. 2 (2015): 145–53. http://dx.doi.org/10.1016/j.artmed.2015.05.007.

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T., Mathu, and Raimond Kumudha. "A novel deep learning architecture for drug named entity recognition." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 6 (2021): 1884–91. https://doi.org/10.12928/telkomnika.v19i6.21667.

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Drug named entity recognition (DNER) becomes the prerequisite of other medical relation extraction systems. Existing approaches to automatically recognize drug names includes rule-based, machine learning (ML) and deep learning (DL) techniques. DL techniques have been verified to be the state-of-the-art as it is independent of handcrafted features. The previous DL methods based on word embedding input representation uses the same vector representation for an entity irrespective of its context in different sentences and hence could not capture the context properly. Also, identification of the n-

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Mathu, T., and Kumudha Raimond. "A novel deep learning architecture for drug named entity recognition." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 6 (2021): 1884. http://dx.doi.org/10.12928/telkomnika.v19i6.21667.

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Kang, Keming, Shengwei Tian, and Long Yu. "Named entity recognition of local adverse drug reactions in Xinjiang based on transfer learning." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 8899–914. http://dx.doi.org/10.3233/jifs-201017.

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For deep learning’s insufficient learning ability of a small amount of data in the Chinese named entity recognition based on deep learning, this paper proposes a named entity recognition of local adverse drug reactions based on Adversarial Transfer Learning, and constructs a neural network model ASAIBC consisting of Adversarial Transfer Learning, Self-Attention, independently recurrent neural network (IndRNN), Bi-directional long short-term memory (BiLSTM) and conditional random field (CRF). However, of the task of Chinese named entity recognition (NER), there are only few open labeled data se

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Xiong, Wangping, Jun Cao, Xian Zhou, et al. "Design and Evaluation of a Prescription Drug Monitoring Program for Chinese Patent Medicine based on Knowledge Graph." Evidence-Based Complementary and Alternative Medicine 2021 (July 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/9970063.

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Background. Chinese patent medicines are increasingly used clinically, and the prescription drug monitoring program is an effective tool to promote drug safety and maintain health. Methods. We constructed a prescription drug monitoring program for Chinese patent medicines based on knowledge graphs. First, we extracted the key information of Chinese patent medicines, diseases, and symptoms from the domain-specific corpus by the information extraction. Second, based on the extracted entities and relationships, a knowledge graph was constructed to form a rule base for the monitoring of data. Then

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Wang, Qi, and Xiyou Su. "Research on Named Entity Recognition Methods in Chinese Forest Disease Texts." Applied Sciences 12, no. 8 (2022): 3885. http://dx.doi.org/10.3390/app12083885.

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Named entity recognition of forest diseases plays a key role in knowledge extraction in the field of forestry. The aim of this paper is to propose a named entity recognition method based on multi-feature embedding, a transformer encoder, a bi-gated recurrent unit (BiGRU), and conditional random fields (CRF). According to the characteristics of the forest disease corpus, several features are introduced here to improve the method’s accuracy. In this paper, we analyze the characteristics of forest disease texts; carry out pre-processing, labeling, and extraction of multiple features; and construc

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Meenachisundaram, Thiyagu, and Manjula Dhanabalachandran. "Biomedical Named Entity Recognition Using the SVM Methodologies and bio Tagging Schemes." Revista de Chimie 72, no. 4 (2021): 52–64. http://dx.doi.org/10.37358/rc.21.4.8456.

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Biomedical Named Entity Recognition (BNER) is identification of entities such as drugs, genes, and chemicals from biomedical text, which help in information extraction from the domain literature. It would allow extracting information such as drug profiles, similar or related drugs and associations between drugs and their targets. This venue presents opportunities for improvement even though many machine learning methods have been applied. The efficiency can be improved in case of biological related chemical entities as there are varied structure and properties. This new approach combines two s

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Liang, Jun, Xuemei Xian, Xiaojun He, et al. "A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text." Journal of Healthcare Engineering 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/4898963.

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Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-b

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Azhar, Daris, Robert Kurniawan, Waris Marsisno, Budi Yuniarto, Sukim Sukim, and Sugiarto Sugiarto. "Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 375–82. https://doi.org/10.11591/ijai.v13.i1.pp375-382.

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The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) architecture because it can pro

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Azhar, Daris, Robert Kurniawan, Waris Marsisno, Budi Yuniarto, Sukim Sukim, and Sugiarto Sugiarto. "Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 375. http://dx.doi.org/10.11591/ijai.v13.i1.pp375-382.

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<span lang="EN-US">The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) archit

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Gong, Lejun, Zhifei Zhang, and Shiqi Chen. "Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining." Journal of Healthcare Engineering 2020 (November 24, 2020): 1–8. http://dx.doi.org/10.1155/2020/8829219.

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Background. Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods. Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embeddin

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Raza, Shaina, Deepak John Reji, Femi Shajan, and Syed Raza Bashir. "Large-scale application of named entity recognition to biomedicine and epidemiology." PLOS Digital Health 1, no. 12 (2022): e0000152. http://dx.doi.org/10.1371/journal.pdig.0000152.

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Background Despite significant advancements in biomedical named entity recognition methods, the clinical application of these systems continues to face many challenges: (1) most of the methods are trained on a limited set of clinical entities; (2) these methods are heavily reliant on a large amount of data for both pre-training and prediction, making their use in production impractical; (3) they do not consider non-clinical entities, which are also related to patient’s health, such as social, economic or demographic factors. Methods In this paper, we develop Bio-Epidemiology-NER (https://pypi.

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Wu, Heng-Yi, Deshun Lu, Mustafa Hyder, et al. "DrugMetab: An Integrated Machine Learning and Lexicon Mapping Named Entity Recognition Method for Drug Metabolite." CPT: Pharmacometrics & Systems Pharmacology 7, no. 11 (2018): 709–17. http://dx.doi.org/10.1002/psp4.12340.

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Hou, Wen-Juan, and Bamfa Ceesay. "Exploring the Adaptation of Recurrent Neural Network Approaches for Extracting Drug–Drug Interactions from Biomedical Text." International Journal of Machine Learning and Computing 11, no. 4 (2021): 267–73. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1046.

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Information extraction (IE) is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several activities, such as named entity recognition, event extraction, relationship discovery, and document classification, with the overall goal of translating text into a more structured form. Information on the changes in the effect of a drug, when taken in combination with a second drug, is known as drug–drug interaction (DDI). DDIs can delay, decrease, or enhance absorption of drugs and thus decrease or increase their effi

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Guo, Xuchao, Xia Hao, Zhan Tang, et al. "ACE-ADP: Adversarial Contextual Embeddings Based Named Entity Recognition for Agricultural Diseases and Pests." Agriculture 11, no. 10 (2021): 912. http://dx.doi.org/10.3390/agriculture11100912.

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Entity recognition tasks, which aim to utilize the deep learning-based models to identify the agricultural diseases and pests-related nouns such as the names of diseases, pests, and drugs from the texts collected on the internet or input by users, are a fundamental component for agricultural knowledge graph construction and question-answering, which will be implemented as a web application and provide the general public with solutions for agricultural diseases and pest control. Nonetheless, there are still challenges: (1) the polysemous problem needs to be further solved, (2) the quality of th

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Yi, Fen, Hong Liu, You Wang, et al. "Medical Named Entity Recognition Fusing Part-of-Speech and Stroke Features." Applied Sciences 13, no. 15 (2023): 8913. http://dx.doi.org/10.3390/app13158913.

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It is highly significant from a research standpoint and a valuable practice to identify diseases, symptoms, drugs, examinations, and other medical entities in medical text data to support knowledge maps, question and answer systems, and other downstream tasks that can provide the public with knowledgeable answers. However, when contrasted with other languages like English, Chinese words lack a distinct dividing line, and medical entities have problems such as long length and multiple entity types nesting. Therefore, to address these issues, this study suggests a medical named entity recognitio

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Batbaatar, Erdenebileg, and Keun Ho Ryu. "Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach." International Journal of Environmental Research and Public Health 16, no. 19 (2019): 3628. http://dx.doi.org/10.3390/ijerph16193628.

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Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named

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Zhu, Xun, and Hong Tao Deng. "Research of Drug Name Entity Recognition Based on Constructed Dictionary and Conditional Random Field." Applied Mechanics and Materials 665 (October 2014): 739–44. http://dx.doi.org/10.4028/www.scientific.net/amm.665.739.

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Drug name entity recognition (NER) is an important foundation of information extraction, automatic question answering, machine translation and information retrieval and other natural language processing technology based on the medical literature. This paper presents a method combined a constructed dictionary and conditional random field model to identify the drug entity. The proposed method has good performance in DDIExtraction 2013 evaluation corpus. //

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Filannino, Michele, and Özlem Uzuner. "Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks." Yearbook of Medical Informatics 27, no. 01 (2018): 184–92. http://dx.doi.org/10.1055/s-0038-1667079.

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Objectives: To review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies. Methods: We harvested the literature by using Google Scholar and PubMed Central to retrieve all shared tasks organized since 2015 on clinical NLP problems on English data. Results: We surveyed 17 shared tasks. We grouped the data into four types (synthetic, drug labels, social data, and clinical data) which are correlated with size and sensitivity. We found named entity recognition

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Yang, Xi, Jiang Bian, Ruogu Fang, Ragnhildur I. Bjarnadottir, William R. Hogan, and Yonghui Wu. "Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting." Journal of the American Medical Informatics Association 27, no. 1 (2019): 65–72. http://dx.doi.org/10.1093/jamia/ocz144.

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Abstract Objective To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge. Materials and Methods We developed a novel clinical named entity recognition method based on an recurrent convolutional neural network and compared it to a recurrent neural network implemented using the long-short term memory architecture, explored methods to integrate medical knowledge as embedding layers in neural networks, and investigated 3 machine learning models, including support ve

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Wu, Hong, Jiatong Ji, Haimei Tian, et al. "Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model." JMIR Medical Informatics 9, no. 12 (2021): e26407. http://dx.doi.org/10.2196/26407.

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Background With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective This study describes how to identify ADR-related information from Chinese ADE reports. Methods Our study established an efficient automated tool, na

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Yang, Hangzhou, and Huiying Gao. "Toward Sustainable Virtualized Healthcare: Extracting Medical Entities from Chinese Online Health Consultations Using Deep Neural Networks." Sustainability 10, no. 9 (2018): 3292. http://dx.doi.org/10.3390/su10093292.

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Increasingly popular virtualized healthcare services such as online health consultations have significantly changed the way in which health information is sought, and can alleviate geographic barriers, time constraints, and medical resource shortage problems. These online patient–doctor communications have been generating abundant amounts of healthcare-related data. Medical entity extraction from these data is the foundation of medical knowledge discovery, including disease surveillance and adverse drug reaction detection, which can potentially enhance the sustainability of healthcare. Previou

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Chen, Yao, Changjiang Zhou, Tianxin Li, et al. "Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training." Journal of Biomedical Informatics 96 (August 2019): 103252. http://dx.doi.org/10.1016/j.jbi.2019.103252.

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Alshahrani, Mona, Abdullah Almansour, Asma Alkhaldi, et al. "Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications." PeerJ 10 (April 4, 2022): e13061. http://dx.doi.org/10.7717/peerj.13061.

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Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normaliza

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Sboev, Alexander, Sanna Sboeva, Ivan Moloshnikov, et al. "Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models." Applied Sciences 12, no. 1 (2022): 491. http://dx.doi.org/10.3390/app12010491.

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The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation

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T, Mathu. "A hybrid drug named entity recognition framework for real time pubmed data using deep learning and text summarization techniques." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 8 (2023): 108–11. http://dx.doi.org/10.15199/48.2023.08.18.

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Li, Fei, Yonghao Jin, Weisong Liu, Bhanu Pratap Singh Rawat, Pengshan Cai, and Hong Yu. "Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study." JMIR Medical Informatics 7, no. 3 (2019): e14830. http://dx.doi.org/10.2196/14830.

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Background The bidirectional encoder representations from transformers (BERT) model has achieved great success in many natural language processing (NLP) tasks, such as named entity recognition and question answering. However, little prior work has explored this model to be used for an important task in the biomedical and clinical domains, namely entity normalization. Objective We aim to investigate the effectiveness of BERT-based models for biomedical or clinical entity normalization. In addition, our second objective is to investigate whether the domains of training data influence the perform

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Lamurias, Andre, João D. Ferreira, and Francisco M. Couto. "Identifying interactions between chemical entities in biomedical text." Journal of Integrative Bioinformatics 11, no. 3 (2014): 1–16. http://dx.doi.org/10.1515/jib-2014-247.

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Summary Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, “Identifying Interactions between Chemical Entities” (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results com

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Chen, Weisi, Pengxiang Qiu, and Francesco Cauteruccio. "MedNER: A Service-Oriented Framework for Chinese Medical Named-Entity Recognition with Real-World Application." Big Data and Cognitive Computing 8, no. 8 (2024): 86. http://dx.doi.org/10.3390/bdcc8080086.

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Named-entity recognition (NER) is a crucial task in natural language processing, especially for extracting meaningful information from unstructured text data. In the healthcare domain, accurate NER can significantly enhance patient care by enabling efficient extraction and analysis of clinical information. This paper presents MedNER, a novel service-oriented framework designed specifically for medical NER in Chinese medical texts. MedNER leverages advanced deep learning techniques and domain-specific linguistic resources to achieve good performance in identifying diabetes-related entities such

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Sakhovskiy, Andrey Sergeyevich, та Elena Viktorovna Tutubalina. "Сross-lingual transfer learning in drug-related information extraction from user-generated texts". Proceedings of the Institute for System Programming of the RAS 33, № 6 (2021): 217–28. http://dx.doi.org/10.15514/ispras-2021-33(6)-15.

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Aggregating knowledge about drug, disease, and drug reaction entities across a broader range of domains and languages is critical for information extraction (IE) applications. In this work, we present a fine-grained evaluation intended to understand the efficiency of multilingual BERT-based models for biomedical named entity recognition (NER) and multi-label sentence classification tasks. We investigate the role of transfer learning (TL) strategies between two English corpora and a novel annotated corpus of Russian reviews about drug therapy. Labels for sentences include health-related issues

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Murphy, Rachel M., Joanna E. Klopotowska, Nicolette F. de Keizer, et al. "Adverse drug event detection using natural language processing: A scoping review of supervised learning methods." PLOS ONE 18, no. 1 (2023): e0279842. http://dx.doi.org/10.1371/journal.pone.0279842.

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To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research an

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Wen, Chaojie, Tao Chen, Xudong Jia, and Jiang Zhu. "Medical Named Entity Recognition from Un-labelled Medical Records based on Pre-trained Language Models and Domain Dictionary." Data Intelligence 3, no. 3 (2021): 402–17. http://dx.doi.org/10.1162/dint_a_00105.

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Medical named entity recognition (NER) is an area in which medical named entities are recognized from medical texts, such as diseases, drugs, surgery reports, anatomical parts, and examination documents. Conventional medical NER methods do not make full use of un-labelled medical texts embedded in medical documents. To address this issue, we proposed a medical NER approach based on pre-trained language models and a domain dictionary. First, we constructed a medical entity dictionary by extracting medical entities from labelled medical texts and collecting medical entities from other resources,

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Herrero-Zazo, María, Isabel Segura-Bedmar, Janna Hastings, and Paloma Martínez. "Application of Domain Ontologies to Natural Language Processing." International Journal of Information Retrieval Research 5, no. 3 (2015): 19–38. http://dx.doi.org/10.4018/ijirr.2015070102.

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Natural Language Processing (NLP) techniques can provide an interesting way to mine the growing biomedical literature, and a promising approach for new knowledge discovery. However, the major bottleneck in this area is that these systems rely on specific resources providing the domain knowledge. Domain ontologies provide a contextual framework and a semantic representation of the domain, and they can contribute to a better performance of current NLP systems. However, their contribution to information extraction has not been well studied yet. The aim of this paper is to provide insights into th

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Christopoulou, Fenia, Thy Thy Tran, Sunil Kumar Sahu, Makoto Miwa, and Sophia Ananiadou. "Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods." Journal of the American Medical Informatics Association 27, no. 1 (2019): 39–46. http://dx.doi.org/10.1093/jamia/ocz101.

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AbstractObjectiveIdentification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.Materials and MethodsWe proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term mem

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Yanagisawa, Yuki, Satoshi Watabe, Sakura Yokoyama, et al. "Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition." JMIR Cancer 11 (March 11, 2025): e69663. https://doi.org/10.2196/69663.

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Background Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named en

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Edegbe, Glory Nosawaru, and Muobonuvie Christabel Tone. "Development of an AI-based Application for Counterfeit Medicine Detection in the Nigerian Drug Market." International Journal of Innovative Computing 15, no. 1 (2025): 17–27. https://doi.org/10.11113/ijic.v15n1.486.

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Counterfeit medicines pose a significant threat to public health worldwide, creating a necessity for detection systems to ensure consumer safety. This research focuses on developing a web-based application using computer vision and Natural Language Processing (NLP) techniques for counterfeit medicine detection. The application integrates logo detection, Optical Character Recognition (OCR), and spell-checking functionalities to validate the authenticity of pharmaceutical products and packaging. By utilizing transfer learning on the YOLO-NAS model and leveraging the Microsoft Common Objects in C

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Preiss, Alexander, Peter Baumgartner, Mark J. Edlund, and Georgiy V. Bobashev. "Using Named Entity Recognition to Identify Substances Used in the Self-medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data." JMIR Formative Research 6, no. 3 (2022): e33919. http://dx.doi.org/10.2196/33919.

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Background The cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances that people use or their effects. Objective The aim of this study is to validate a methodology for identifying the substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit. Methods We developed a named entity recognition model to extract substances and effects from nearly 4

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Stojanov, Riste, Gorjan Popovski, Gjorgjina Cenikj, Barbara Koroušić Seljak, and Tome Eftimov. "A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation." Journal of Medical Internet Research 23, no. 8 (2021): e28229. http://dx.doi.org/10.2196/28229.

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Background Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and machine learning to enable biomedical information extraction. However, machine learning in food science domains remains inadequately resourced, which brings to attention the problem of developing methods for food information extraction. There are only f

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Sadikin, Mujiono, Mohamad Ivan Fanany, and T. Basaruddin. "A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text." Computational Intelligence and Neuroscience 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/3483528.

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One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper present

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Wegner, Philipp, Holger Fröhlich, and Sumit Madan. "Evaluating knowledge fusion models on detecting adverse drug events in text." PLOS Digital Health 4, no. 3 (2025): e0000468. https://doi.org/10.1371/journal.pdig.0000468.

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Detecting adverse drug events (ADE) of drugs that are already available on the market is an essential part of the pharmacovigilance work conducted by both medical regulatory bodies and the pharmaceutical industry. Concerns regarding drug safety and economic interests serve as motivating factors for the efforts to identify ADEs. Hereby, social media platforms play an important role as a valuable source of reports on ADEs, particularly through collecting posts discussing adverse events associated with specific drugs. We aim with our study to assess the effectiveness of knowledge fusion approache

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Song, Min, Seung Han Baek, Go Eun Heo, and Jeong-Hoon Lee. "Inferring Drug-Protein–Side Effect Relationships from Biomedical Text." Genes 10, no. 2 (2019): 159. http://dx.doi.org/10.3390/genes10020159.

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Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. Materials and Methods: We extracted three types of relationships—drug-protein, protein-protein, and protein–side eff

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Shrivastava, Dharmsheel, Malathi H. Malathi.H, Swarna Swetha Kolaventi, et al. "Integrating Natural Language Processing in Medical Information Science for Clinical Text Analysis." Seminars in Medical Writing and Education 3 (December 31, 2024): 513. https://doi.org/10.56294/mw2024513.

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The rapid digitization of healthcare data has led to an exponential increase in unstructured clinical text, necessitating the integration of Natural Language Processing (NLP) in Medical Information Science. This research explores deep learning-based NLP techniques for clinical text analysis, focusing on Named Entity Recognition (NER), disease classification, adverse drug reaction detection, and clinical text summarization. The study leverages state-of-the-art transformer models such as BioBERT, ClinicalBERT, and GPT-4 Medical, which demonstrate superior performance in extracting key medical en

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NISHANTH JOSEPH PAULRAJ. "Natural Language Processing on Clinical Notes: Advanced Techniques for Risk Prediction and Summarization." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 494–502. https://doi.org/10.32996/jcsts.2025.7.3.56.

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This article explores the application of Natural Language Processing (NLP) techniques to clinical notes, focusing specifically on risk prediction and automated summarization capabilities. Healthcare institutions generate vast amounts of unstructured clinical text that contains critical information not captured in structured data fields. It examines how modern NLP approaches, including named entity recognition, text classification, and clinical summarization, can extract actionable insights from narrative documentation. It discusses specialized language models like BioBERT, ClinicalBERT, and Me

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Qin, Xuan, Xinzhi Yao, and Jingbo Xia. "A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study." JMIR Medical Informatics 9, no. 6 (2021): e28247. http://dx.doi.org/10.2196/28247.

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Background Natural language processing has long been applied in various applications for biomedical knowledge inference and discovery. Enrichment analysis based on named entity recognition is a classic application for inferring enriched associations in terms of specific biomedical entities such as gene, chemical, and mutation. Objective The aim of this study was to investigate the effect of pathway enrichment evaluation with respect to biomedical text-mining results and to develop a novel metric to quantify the effect. Methods Four biomedical text mining methods were selected to represent natu

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Yada, Shuntaro, Tomohiro Nishiyama, Shoko Wakamiya, et al. "Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs." PLOS ONE 19, no. 9 (2024): e0310432. http://dx.doi.org/10.1371/journal.pone.0310432.

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Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured data represented by text (e.g., discharge summaries) are not effectively utilized because of the difficulty in extracting medical information. We evaluated the information gained by supplementing structured data with clinical concepts extracted from unstructured text by leveraging natural language processing techniques. Using a machine learning-base

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Sorbello, Alfred, Syed Arefinul Haque, Rashedul Hasan, et al. "Artificial Intelligence–Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study." JMIR AI 2 (July 18, 2023): e45000. http://dx.doi.org/10.2196/45000.

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Background The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources. Objective Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE)

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Ma, Meng, Kyeryoung Lee, Yun Mai, et al. "Extracting longitudinal anticancer treatments at scale using deep natural language processing and temporal reasoning." Journal of Clinical Oncology 39, no. 15_suppl (2021): e18747-e18747. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e18747.

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e18747 Background: Accurate longitudinal cancer treatments are vital for establishing primary endpoints such as outcome as well as for the investigation of adverse events. However, many longitudinal therapeutic regimens are not well captured in structured electronic health records (EHRs). Thus, their recognition in unstructured data such as clinical notes is critical to gain an accurate description of the real-world patient treatment journey. Here, we demonstrate a scalable approach to extract high-quality longitudinal cancer treatments from lung cancer patients' clinical notes using a Bidirec

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Rawat, Ashish, Mudasir Ahmad Wani, Mohammed ElAffendi, Ali Shariq Imran, Zenun Kastrati, and Sher Muhammad Daudpota. "Drug Adverse Event Detection Using Text-Based Convolutional Neural Networks (TextCNN) Technique." Electronics 11, no. 20 (2022): 3336. http://dx.doi.org/10.3390/electronics11203336.

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With the rapid advancement in healthcare, there has been exponential growth in the healthcare records stored in large databases to help researchers, clinicians, and medical practitioner’s for optimal patient care, research, and trials. Since these studies and records are lengthy and time consuming for clinicians and medical practitioners, there is a demand for new, fast, and intelligent medical information retrieval methods. The present study is a part of the project which aims to design an intelligent medical information retrieval and summarization system. The whole system comprises three mai

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Mitrofan, Maria, Verginica Barbu Mititelu, and Grigorina Mitrofan. "Towards the Construction of a Gold Standard Biomedical Corpus for the Romanian Language." Data 3, no. 4 (2018): 53. http://dx.doi.org/10.3390/data3040053.

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Gold standard corpora (GSCs) are essential for the supervised training and evaluation of systems that perform natural language processing (NLP) tasks. Currently, most of the resources used in biomedical NLP tasks are mainly in English. Little effort has been reported for other languages including Romanian and, thus, access to such language resources is poor. In this paper, we present the construction of the first morphologically and terminologically annotated biomedical corpus of the Romanian language (MoNERo), meant to serve as a gold standard for biomedical part-of-speech (POS) tagging and b

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Hafsah, Saidah Saad, Lailatul Qadri Zakaria, and Ahmad Fadhil Naswir. "Parallel-Based Corpus Annotation for Malay Health Documents." Applied Sciences 13, no. 24 (2023): 13129. http://dx.doi.org/10.3390/app132413129.

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Named entity recognition (NER) is a crucial component of various natural language processing (NLP) applications, particularly in healthcare. It involves accurately identifying and extracting named entities such as medical terms, diseases, and drug names, and healthcare professionals are essential for tasks like clinical text analysis, electronic health record management, and medical research. However, healthcare NER faces challenges, especially in Malay, in which specialized corpora are limited, and no general corpus is available yet. To address this, the paper proposes a method for constructi

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