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Journal articles on the topic 'Medical entity extraction'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kuttaiyapillai, Dhanasekaran, Anand Madasamy, Shobanadevi Ayyavu, and Md Shohel Sayeed. "Clinical named entity extraction for extracting information from medical data." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1722. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1722-1731.

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Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine le

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Dhanasekaran, Kuttaiyapillai Anand Madasamy Shobanadevi Ayyavu Md Shohel Sayeed. "Clinical named entity extraction for extracting information from medical data." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1722–31. https://doi.org/10.11591/ijeecs.v35.i3.pp1722-1731.

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Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the trad

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Zhang, Qinghui, Meng Wu, Pengtao Lv, Mengya Zhang, and Lei Lv. "Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information." Applied Sciences 12, no. 19 (2022): 9781. http://dx.doi.org/10.3390/app12199781.

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Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency struc

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Takeuchi, Koichi, and Nigel Collier. "Bio-medical entity extraction using support vector machines." Artificial Intelligence in Medicine 33, no. 2 (2005): 125–37. http://dx.doi.org/10.1016/j.artmed.2004.07.019.

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Zhang, Qinghui, Yaya Sun, Pengtao Lv, et al. "RoGraphER: Enhanced Extraction of Chinese Medical Entity Relationships Using RoFormer Pre-Trained Model and Weighted Graph Convolution." Electronics 13, no. 15 (2024): 2892. http://dx.doi.org/10.3390/electronics13152892.

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Unstructured Chinese medical texts are rich sources of entity and relational information. The extraction of entity relationships from medical texts is pivotal for the construction of medical knowledge graphs and aiding healthcare professionals in making swift and informed decisions. However, the extraction of entity relationships from these texts presents a formidable challenge, notably due to the issue of overlapping entity relationships. This study introduces a novel extraction model that leverages RoFormer’s rotational position encoding (RoPE) technique for an efficient implementation of re

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Xie, Zhe, Yuanyuan Yang, Mingqing Wang, et al. "Introducing Information Extraction to Radiology Information Systems to Improve the Efficiency on Reading Reports." Methods of Information in Medicine 58, no. 02/03 (2019): 094–106. http://dx.doi.org/10.1055/s-0039-1694992.

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Abstract Background Radiology reports are a permanent record of patient's health information often used in clinical practice and research. Reading radiology reports is common for clinicians and radiologists. However, it is laborious and time-consuming when the amount of reports to be read is large. Assisting clinicians to locate and assimilate the key information of reports is of great significance for improving the efficiency of reading reports. There are few studies on information extraction from Chinese medical texts and its application in radiology information systems (RIS) for efficiency

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Lai, Qinghan, Zihan Zhou, and Song Liu. "Joint Entity-Relation Extraction via Improved Graph Attention Networks." Symmetry 12, no. 10 (2020): 1746. http://dx.doi.org/10.3390/sym12101746.

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Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in an end-to-end manner. At present, compared with the named entity recognition task, the relation extraction task performs poorly on complex text. To solve this problem, we proposed a novel joint model named extracting Entity-Relations viaImproved Graph Attention networks (ERIGAT), which enhances the ability of the relation extraction task. In our proposed model, we introduced the graph attention network to extract entit

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Padmanandam, Kayal, Nikitha Pitla, and Yeshasvi Mogula. "NAMED ENTITY RECOGNITION FOR MEDICAL DATA EXTRACTION USING BIOBERT." Proceedings on Engineering Sciences 6, no. 4 (2024): 1757–64. https://doi.org/10.24874/pes.si.24.03.012.

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Shi, Xue, Yingping Yi, Ying Xiong, et al. "Extracting entities with attributes in clinical text via joint deep learning." Journal of the American Medical Informatics Association 26, no. 12 (2019): 1584–91. http://dx.doi.org/10.1093/jamia/ocz158.

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Abstract Objective Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extraction. One problem of pipeline methods is that errors from entity recognition are unavoidably passed to relation extraction. We propose a novel joint deep learning method to recognize clinical entities or attributes and extract entity-attribute relations simultaneously. Materials and Method

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Chang, Hongyang, Hongying Zan, Tongfeng Guan, Kunli Zhang, and Zhifang Sui. "Application of cascade binary pointer tagging in joint entity and relation extraction of Chinese medical text." Mathematical Biosciences and Engineering 19, no. 10 (2022): 10656–72. http://dx.doi.org/10.3934/mbe.2022498.

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<abstract><p>Extracting relational triples from unstructured medical texts can provide a basis for the construction of large-scale medical knowledge graphs. The cascade binary pointer tagging network (CBPTN) shows excellent performance in the joint entity and relation extraction, so we try to explore its effectiveness in the joint entity and relation extraction of Chinese medical texts. In this paper, we propose two models based on the CBPTN: CBPTN with conditional layer normalization (Cas-CLN) and biaffine transformation-based CBPTN with multi-head selection (BTCAMS). Cas-CLN uses

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Chang, Hongyang, Hongying Zan, Shuai Zhang, Bingfei Zhao, and Kunli Zhang. "Construction of cardiovascular information extraction corpus based on electronic medical records." Mathematical Biosciences and Engineering 20, no. 7 (2023): 13379–97. http://dx.doi.org/10.3934/mbe.2023596.

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<abstract><p>Cardiovascular disease has a significant impact on both society and patients, making it necessary to conduct knowledge-based research such as research that utilizes knowledge graphs and automated question answering. However, the existing research on corpus construction for cardiovascular disease is relatively limited, which has hindered further knowledge-based research on this disease. Electronic medical records contain patient data that span the entire diagnosis and treatment process and include a large amount of reliable medical information. Therefore, we collected e

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Ren, Yaqian. "Utilizing BERT for entity relationship extraction in Chinese medical texts." Applied and Computational Engineering 35, no. 1 (2024): 229–33. http://dx.doi.org/10.54254/2755-2721/35/20230398.

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The Chinese medical sector has been somewhat lacking in knowledge graphs, a deficiency this study aims to address. By leveraging the prowess of the BERT pre-training model, a two-tier approach has been innovated that utilizes separate pre-trained encoders for both entity and relational models. These models are intricately linked: the output from the entity model seamlessly flows into the relational one, making it possible to adeptly extract entity relationships from Chinese medical texts. This research is anchored in the CMeIE dataset, sourced from the esteemed CHIP (China Health Information P

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Li, Qiuyue, Hao Sheng, Mingxue Sheng, and Honglin Wan. "MSIE-Net: Associative Entity-Based Multi-Stage Network for Structured Information Extraction from Reports." Applied Sciences 14, no. 4 (2024): 1668. http://dx.doi.org/10.3390/app14041668.

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Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured

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Kang, Yufeng, Yang Yan, and Wenbo Huang. "Chinese Medical Named Entity Recognition Based on Context-Dependent Perception and Novel Memory Units." Applied Sciences 14, no. 18 (2024): 8471. http://dx.doi.org/10.3390/app14188471.

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Medical named entity recognition (NER) focuses on extracting and classifying key entities from medical texts. Through automated medical information extraction, NER can effectively improve the efficiency of electronic medical record analysis, medical literature retrieval, and intelligent medical question–answering systems, enabling doctors and researchers to obtain the required medical information more quickly and thereby helping to improve the accuracy of diagnosis and treatment decisions. The current methods have certain limitations in dealing with contextual dependencies and entity memory an

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Zou, Yuwei, Jinguang Gu, and Haidong Fu. "Medical entity and attributes extraction system based on relation annotation." Wuhan University Journal of Natural Sciences 21, no. 2 (2016): 145–50. http://dx.doi.org/10.1007/s11859-016-1151-z.

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Qin, Chaoping, Zhanxiang Wang, Jingran Zhao, Luyi Liu, Feng Xiao, and Yi Han. "A Novel Rational Medicine Use System Based on Domain Knowledge Graph." Electronics 13, no. 16 (2024): 3156. http://dx.doi.org/10.3390/electronics13163156.

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Medication errors, which could often be detected in advance, are a significant cause of patient deaths each year, highlighting the critical importance of medication safety. The rapid advancement of data analysis technologies has made intelligent medication assistance applications possible, and these applications rely heavily on medical knowledge graphs. However, current knowledge graph construction techniques are predominantly focused on general domains, leaving a gap in specialized fields, particularly in the medical domain for medication assistance. The specialized nature of medical knowledg

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Chen, Xianglong, Chunping Ouyang, Yongbin Liu, and Yi Bu. "Improving the Named Entity Recognition of Chinese Electronic Medical Records by Combining Domain Dictionary and Rules." International Journal of Environmental Research and Public Health 17, no. 8 (2020): 2687. http://dx.doi.org/10.3390/ijerph17082687.

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Electronic medical records are an integral part of medical texts. Entity recognition of electronic medical records has triggered many studies that propose many entity extraction methods. In this paper, an entity extraction model is proposed to extract entities from Chinese Electronic Medical Records (CEMR). In the input layer of the model, we use word embedding and dictionary features embedding as input vectors, where word embedding consists of a character representation and a word representation. Then, the input vectors are fed to the bidirectional long short-term memory to capture contextual

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Ghoulam, Aicha, Fatiha Barigou, and Ghalem Belalem. "Information Extraction in the Medical Domain." Journal of Information Technology Research 8, no. 2 (2015): 1–15. http://dx.doi.org/10.4018/jitr.2015040101.

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Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyse texts written in natural language to extract structured and useful information such as named entities and semantic relations between them. Information extraction is an important task in a diverse set of applications like bio-medical literature mining, customer care, community websites, personal information management and so on. In this paper, the authors focus only on information extraction from clinical reports. The two most fundamental tasks in information extraction are discussed; namely, named e

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Malashin, Ivan, Igor Masich, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, and Aleksei Borodulin. "Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports." Machine Learning and Knowledge Extraction 6, no. 2 (2024): 1361–77. http://dx.doi.org/10.3390/make6020064.

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This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) techniques like named entity recognition (NER). The primary aim was to develop an adaptive model for efficient text extraction from medical report images. This involved utilizing a genetic algorithm (GA) to fine-tune optical character recognition (OCR) hyperparameters, ensuring maximal text extraction length, followed by NER processing to categorize

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Wang, Anli, Linyi Li, Xuehong Wu, et al. "Entity relation extraction in the medical domain: based on data augmentation." Annals of Translational Medicine 10, no. 19 (2022): 1061. http://dx.doi.org/10.21037/atm-22-3991.

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Wang, Ling, Minglei Shan, Tie Hua Zhou, and Keun Ho Ryu. "Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data." Applied Sciences 13, no. 20 (2023): 11151. http://dx.doi.org/10.3390/app132011151.

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The task of accurately identifying medical entities and extracting entity relationships from large-scale medical text data has become a hot topic in recent years, aiming to mine potential rules and knowledge. How to conduct in-depth context analysis from biomedical texts, such as medical procedures, diseases, therapeutic drugs, and disease characteristics, and identify valuable knowledge in the medical field is our main research content. Through the process of knowledge mining, a deeper understanding of the complex relationships between various factors in diseases can be gained, which holds si

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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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Singh, Ajay Kumar, Ihtiram Raza Khan, Shakir Khan, Kumud Pant, Sandip Debnath, and Shahajan Miah. "Multichannel CNN Model for Biomedical Entity Reorganization." BioMed Research International 2022 (March 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/5765629.

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Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowi

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Gao, Yan, Yandong Wang, Patrick Wang, and Lei Gu. "Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network." International Journal of Environmental Research and Public Health 17, no. 5 (2020): 1614. http://dx.doi.org/10.3390/ijerph17051614.

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The resident admit notes (RANs) in electronic medical records (EMRs) is first-hand information to study the patient’s condition. Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chinese electronic medical records, each medical entity contains not only word information but also rich character information. Effective combination of words and characters is very important for medical entity extraction. We propose a medical entity recognition model based on a character and word attention-enhanced (CWAE) neural network for Chinese RANs

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Sallauka, Rigon, Umut Arioz, Matej Rojc, and Izidor Mlakar. "Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages." Applied Sciences 15, no. 10 (2025): 5585. https://doi.org/10.3390/app15105585.

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Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 mod

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Mlakar, Izidor, Rigon Sallauka, Matej Rojc, and Umut Arioz. "Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages." Applied Sciences 15, no. 10 (2025): 5585. https://doi.org/10.3390/app15105585.

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Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 mod

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Wang, Yu, Yining Sun, Zuchang Ma, Lisheng Gao, and Yang Xu. "Named Entity Recognition in Chinese Medical Literature Using Pretraining Models." Scientific Programming 2020 (September 9, 2020): 1–9. http://dx.doi.org/10.1155/2020/8812754.

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The medical literature contains valuable knowledge, such as the clinical symptoms, diagnosis, and treatments of a particular disease. Named Entity Recognition (NER) is the initial step in extracting this knowledge from unstructured text and presenting it as a Knowledge Graph (KG). However, the previous approaches of NER have often suffered from small-scale human-labelled training data. Furthermore, extracting knowledge from Chinese medical literature is a more complex task because there is no segmentation between Chinese characters. Recently, the pretraining models, which obtain representation

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Yang, Yousen, Jijun Tong, and Qingli Zhou. "Modeling of joint extraction of entity relationships in clinical electronic medical records." Computers in Biology and Medicine 182 (November 2024): 109161. http://dx.doi.org/10.1016/j.compbiomed.2024.109161.

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Jia, Qi, Dezheng Zhang, Haifeng Xu, and Yonghong Xie. "Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision." JMIR Medical Informatics 9, no. 6 (2021): e28219. http://dx.doi.org/10.2196/28219.

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Background Traditional Chinese medicine (TCM) clinical records contain the symptoms of patients, diagnoses, and subsequent treatment of doctors. These records are important resources for research and analysis of TCM diagnosis knowledge. However, most of TCM clinical records are unstructured text. Therefore, a method to automatically extract medical entities from TCM clinical records is indispensable. Objective Training a medical entity extracting model needs a large number of annotated corpus. The cost of annotated corpus is very high and there is a lack of gold-standard data sets for supervis

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Gu, Jinguang, Daiwen Wang, Danyang Hu, Feng Gao, and Fangfang Xu. "Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback." Applied Sciences 13, no. 5 (2023): 3348. http://dx.doi.org/10.3390/app13053348.

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In medical texts, temporal information describes events and changes in status, such as medical visits and discharges. According to the semantic features, it is classified into simple time and complex time. The current research on time recognition usually focuses on coarse-grained simple time recognition while ignoring fine-grained complex time. To address this problem, based on the semantic concept of complex time in Clinical Time Ontology, we define seven basic features and eleven extraction rules and propose a complex medical time-extraction method. It combines probabilistic soft logic and t

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Hu, Ze, Wenjun Li, and Hongyu Yang. "Named Entity Recognition in Online Medical Consultation Using Deep Learning." Applied Sciences 15, no. 6 (2025): 3033. https://doi.org/10.3390/app15063033.

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Named entity recognition in online medical consultation aims to address the challenge of identifying various types of medical entities within complex and unstructured social text in the context of online medical consultations. This can provide important data support for constructing more powerful online medical consultation knowledge graphs and improving virtual intelligent health assistants. A dataset of 26 medical entity types for named entity recognition for online medical consultations is first constructed. Then, a novel approach for deep named entity recognition in the medical field based

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Qi, Renlong, Pengtao Lv, Qinghui Zhang, and Meng Wu. "Research on Chinese Medical Entity Recognition Based on Multi-Neural Network Fusion and Improved Tri-Training Algorithm." Applied Sciences 12, no. 17 (2022): 8539. http://dx.doi.org/10.3390/app12178539.

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Chinese medical texts contain a large number of medically named entities. Automatic recognition of these medical entities from medical texts is the key to developing medical informatics. In the field of Chinese medical information extraction, annotated Chinese medical text data are very few. In the named entity recognition task, there is insufficient labeled data, which leads to low model recognition performance. Therefore, this paper proposes a Chinese medical entity recognition model based on multi-neural network fusion and the improved Tri-Training algorithm. The model performs semi-supervi

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Zhang, Qinghui, Meng Wu, Pengtao Lv, Mengya Zhang, and Hongwei Yang. "Research on named entity recognition of chinese electronic medical records based on multi-head attention mechanism and character-word information fusion." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 4105–16. http://dx.doi.org/10.3233/jifs-212495.

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In the medical field, Named Entity Recognition (NER) plays a crucial role in the process of information extraction through electronic medical records and medical texts. To address the problems of long distance entity, entity confusion, and difficulty in boundary division in the Chinese electronic medical record NER task, we propose a Chinese electronic medical record NER method based on the multi-head attention mechanism and character-word fusion. This method uses a new character-word joint feature representation based on the pre-training model BERT and self-constructed domain dictionary, whic

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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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Kim, Youngjun, Paul M. Heider, Isabel RH Lally, and Stéphane M. Meystre. "A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System." JMIR Medical Informatics 9, no. 4 (2021): e22797. http://dx.doi.org/10.2196/22797.

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Background Family history information is important to assess the risk of inherited medical conditions. Natural language processing has the potential to extract this information from unstructured free-text notes to improve patient care and decision making. We describe the end-to-end information extraction system the Medical University of South Carolina team developed when participating in the 2019 National Natural Language Processing Clinical Challenge (n2c2)/Open Health Natural Language Processing (OHNLP) shared task. Objective This task involves identifying mentions of family members and obse

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Báez, Pablo, Felipe Bravo-Marquez, Jocelyn Dunstan, Matías Rojas, and Fabián Villena. "Automatic Extraction of Nested Entities in Clinical Referrals in Spanish." ACM Transactions on Computing for Healthcare 3, no. 3 (2022): 1–22. http://dx.doi.org/10.1145/3498324.

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Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus h

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Nash, Anthony, and M. Zameel Cader. "Extraction of CPRD additional clinical data using R." F1000Research 9 (September 11, 2020): 1124. http://dx.doi.org/10.12688/f1000research.26228.1.

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The Clinical Practice Research Datalink is a nation-wide database of primary healthcare data records in England (UK) linked to several health services. A visit to a health practitioner can result in the digital storing of diagnostic and prescription therapeutic information. Access to patient primary care and linked service data depends on the research in mind; however, typically several flat files that describe patient interactions with a health practitioner are delivered. Some of these files will describe additional data such as the result of medical tests and patient lifestyles, denoted coll

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Yang, Jianliang, Yuenan Liu, Minghui Qian, Chenghua Guan, and Xiangfei Yuan. "Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding." Applied Sciences 9, no. 18 (2019): 3658. http://dx.doi.org/10.3390/app9183658.

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Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language M

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Lu, Xiaoqing, Jijun Tong, and Shudong Xia. "Entity relationship extraction from Chinese electronic medical records based on feature augmentation and cascade binary tagging framework." Mathematical Biosciences and Engineering 21, no. 1 (2023): 1342–55. http://dx.doi.org/10.3934/mbe.2024058.

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<abstract> <p>Extracting entity relations from unstructured Chinese electronic medical records is an important task in medical information extraction. However, Chinese electronic medical records mostly have document-level volumes, and existing models are either unable to handle long text sequences or exhibit poor performance. This paper proposes a neural network based on feature augmentation and cascade binary tagging framework. First, we utilize a pre-trained model to tokenize the original text and obtain word embedding vectors. Second, the word vectors are fed into the feature au

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Zulkarneev, Rustem, Nafisa Yusupova, Olga Smetanina, Maya Gayanova, and Alexey Vulfin. "Method and Models of Extraction of Knowledge from Medical Documents." Informatics and Automation 21, no. 6 (2022): 1169–210. http://dx.doi.org/10.15622/ia.21.6.4.

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The paper analyzes the problem of extracting knowledge from clinical recommendations presented in the form of semi-structured corpora of text documents in natural language, taking into account their periodic updating. The considered methods of intellectual analysis of the accumulated arrays of medical data make it possible to automate a number of tasks aimed at improving the quality of medical care due to significant decision support in the treatment process. A brief review of well-known publications has been made, highlighting approaches to automating the construction of ontologies and knowle

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Ravikumar, J., and Kumar P.. Ramakanth. "A framework for named entity recognition of clinical data." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 2 (2020): 946–52. https://doi.org/10.11591/ijeecs.v18.i2.pp946-952.

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With emergence of technologies like big data, the healthcare services are also being explored to apply this technology and reap benefits. Big Data analytics can be implemented as a part of e-health which involves the extrapolation of actionable insights from sources like health knowledge base and health information systems. Present day medical data creates a lot of information consistently. At present, Hospital Information System is a quickly developing innovation. This data is a major asset for getting data from gathering of gigantic measures of surgical information by forcing a few questions

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Ronzhin, L. V., P. A. Astanin, S. E. Rauzina, P. A. Yadgarova, and T. V. Zarubina. "Development of a service for automatically extraction of medical concepts from Russian unstructured texts." Siberian Journal of Clinical and Experimental Medicine 40, no. 2 (2025): 201–10. https://doi.org/10.29001/2073-8552-2025-40-2-201-210.

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Introduction. A significant part of medical data is currently generated and stored in an unstructured (textual) form. One way to process unstructured information is named entity recognition (NER). In the classical view, solving the NER problem within medical texts involves identifying objects or concepts that have a specific context related to the actions or events mentioned in the text. The National Unified Terminological System (NUTS) has been developed since 2022 based on international and federal medical thesauri and other sources. It can be used as the term set for solving problems of thi

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Hou, Ruixuan. "Application of big data technology in the medical field." Advances in Engineering Innovation 8, no. 1 (2024): 70–80. http://dx.doi.org/10.54254/2977-3903/8/2024083.

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This project aims to construct a knowledge graph system applied to the field of traditional Chinese medicine (TCM) by extracting entities (such as drugs, diseases, etc.) and their relationships from TCM medical case data and storing them in a Neo4j database. The project process includes data reading, entity recognition and extraction, data formatting, and data import into the database. The project not only improved the individual's proficiency in Python data processing techniques (including regular expressions and JSON parsing) but also enhanced their skills in knowledge graph construction and

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Yang, Lei, Yufan Fu, and Yu Dai. "BIBC: A Chinese Named Entity Recognition Model for Diabetes Research." Applied Sciences 11, no. 20 (2021): 9653. http://dx.doi.org/10.3390/app11209653.

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In the medical field, extracting medical entities from text by Named Entity Recognition (NER) has become one of the research hotspots. This thesis takes the chapter-level diabetes literature as the research object and uses a deep learning method to extract medical entities in the literature. Based on the deep and bidirectional transformer network structure, the pre-training language model BERT model can solve the problem of polysemous word representation, and supplement the features by large-scale unlabeled data, combined with BiLSTM-CRF model extracts of the long-distance features of sentence

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Abdurxit, Mamatjan, Turdi Tohti, and Askar Hamdulla. "An Efficient Method for Biomedical Entity Linking Based on Inter- and Intra-Entity Attention." Applied Sciences 12, no. 6 (2022): 3191. http://dx.doi.org/10.3390/app12063191.

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Biomedical entity linking is an important research problem for many downstream tasks, such as biomedical intelligent question answering, information retrieval, and information extraction. Biomedical entity linking is the task of mapping mentions in medical texts to standard entities in a given knowledge base. Recently, BERT-based models have achieved state-of-the-art results on the biomedical entity linking task. Although this type of method is effective, it brings challenges for fine-tuning and online services in practical industries due to a large number of model parameters and long inferenc

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Cao, Pei, Zhongtao Yang, Xinlu Li, and Yu Li. "A Character-Word Information Interaction Framework for Natural Language Understanding in Chinese Medical Dialogue Domain." Applied Sciences 14, no. 19 (2024): 8926. http://dx.doi.org/10.3390/app14198926.

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Natural language understanding is a foundational task in medical dialogue systems. However, there are still two key problems to be solved: (1) Multiple meanings of a word lead to ambiguity of intent; (2) character errors make slot entity extraction difficult. To solve the above problems, this paper proposes a character-word information interaction framework (CWIIF) for natural language understanding in the Chinese medical dialogue domain. The CWIIF framework contains an intent information adapter to solve the problem of intent ambiguity caused by multiple meanings of words in the intent detect

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Sugimoto, Kento, Shoya Wada, Shozo Konishi, et al. "Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation." JMIR Medical Informatics 11 (November 14, 2023): e49041-e49041. http://dx.doi.org/10.2196/49041.

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Abstract Background Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports. Objective For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format. Methods Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evalua

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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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Kang, Tian, Shaodian Zhang, Youlan Tang, et al. "EliIE: An open-source information extraction system for clinical trial eligibility criteria." Journal of the American Medical Informatics Association 24, no. 6 (2017): 1062–71. http://dx.doi.org/10.1093/jamia/ocx019.

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Abstract Objective To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0. Materials and Methods EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and ge

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Gupta, Pankaj, Subburam Rajaram, Hinrich Schütze, and Thomas Runkler. "Neural Relation Extraction within and across Sentence Boundaries." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6513–20. http://dx.doi.org/10.1609/aaai.v33i01.33016513.

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Past work in relation extraction mostly focuses on binary relation between entity pairs within single sentence. Recently, the NLP community has gained interest in relation extraction in entity pairs spanning multiple sentences. In this paper, we propose a novel architecture for this task: inter-sentential dependency-based neural networks (iDepNN). iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries. Compared to SVM and neural network baselines, iDepNN is more robus

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