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Gotowe bibliografie tematyczne / Medical entity extraction

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Gotowa bibliografia na temat „Medical entity extraction”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Medical entity extraction"

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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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Rozprawy doktorskie na temat "Medical entity extraction"

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Radovanovic, Aleksandar. "Concept Based Knowledge Discovery from Biomedical Literature." Thesis, Online access, 2009. http://etd.uwc.ac.za/usrfiles/modules/etd/docs/etd_gen8Srv25Nme4_9861_1272229462.pdf.

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Ben, Abacha Asma. "Recherche de réponses précises à des questions médicales : le système de questions-réponses MEANS." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00735612.

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La recherche de réponses précises à des questions formulées en langue naturelle renouvelle le champ de la recherche d'information. De nombreux travaux ont eu lieu sur la recherche de réponses à des questions factuelles en domaine ouvert. Moins de travaux ont porté sur la recherche de réponses en domaine de spécialité, en particulier dans le domaine médical ou biomédical. Plusieurs conditions différentes sont rencontrées en domaine de spécialité comme les lexiques et terminologies spécialisés, les types particuliers de questions, entités et relations du domaine ou les caractéristiques des docum

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Części książek na temat "Medical entity extraction"

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Betina Antony, J., G. S. Mahalakshmi, V. Priyadarshini, and V. Sivagami. "Entity Relation Extraction for Indigenous Medical Text." In Smart Innovations in Communication and Computational Sciences. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8968-8_13.

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Li, Zihao, Mosha Chen, Kangping Yin, et al. "CHIP2022 Shared Task Overview: Medical Causal Entity Relationship Extraction." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4826-0_5.

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Bhardwaj, Priti, Nonita Sharma, and Niyati Baliyan. "An Improved Medical Entity Extraction Method from Annotated Records." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-5703-9_37.

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Ma, Cheng, and Wenkang Huang. "Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0713-5_15.

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Liang, Tao, Shengjun Yuan, Pengfei Zhou, Hangcong Fu, and Huizhe Wu. "Domain Robust Pipeline for Medical Causal Entity and Relation Extraction Task." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4826-0_6.

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Qin, Tianyi, and Yi Guan. "A Bootstrapping Approach to Symptom Entity Extraction on Chinese Electronic Medical Records." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47674-2_34.

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Diomaiuta, Crescenzo, Maria Mercorella, Mario Ciampi, and Giuseppe De Pietro. "Medical Entity and Relation Extraction from Narrative Clinical Records in Italian Language." In Intelligent Interactive Multimedia Systems and Services 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59480-4_13.

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Santiso, Sara, Arantza Casillas, Alicia Pérez, and Maite Oronoz. "Medical Entity Recognition and Negation Extraction: Assessment of NegEx on Health Records in Spanish." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_15.

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Sun, Wei, Shaoxiong Ji, Tuulia Denti, et al. "Weak Supervision and Clustering-Based Sample Selection for Clinical Named Entity Recognition." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43427-3_27.

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AbstractOne of the central tasks of medical text analysis is to extract and structure meaningful information from plain-text clinical documents. Named Entity Recognition (NER) is a sub-task of information extraction that involves identifying predefined entities from unstructured free text. Notably, NER models require large amounts of human-labeled data to train, but human annotation is costly and laborious and often requires medical training. Here, we aim to overcome the shortage of manually annotated data by introducing a training scheme for NER models that uses an existing medical ontology t

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Liu, Zhao, Jian Tong, Jinguang Gu, Kai Liu, and Bo Hu. "A Semi-automated Entity Relation Extraction Mechanism with Weakly Supervised Learning for Chinese Medical Webpages." In Smart Health. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59858-1_5.

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Streszczenia konferencji na temat "Medical entity extraction"

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Hong, Yonglu, and Yanhua Liu. "An Entity Enhancement-Based Approach for Joint Extraction of Entity Relationships in Medical Texts." In 2024 International Conference on Ubiquitous Computing and Communications (IUCC). IEEE, 2024. https://doi.org/10.1109/iucc65928.2024.00036.

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Du, Jinlian, Xiaolin Du, Zhenwei Lu, and Xiao Zhang. "Extraction of Chinese Medical Entity Attribute Values Based on Multi-type Feature Fusion." In 2025 8th International Conference on Information and Computer Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64582.2025.00085.

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Shuang, Chen, Hou Qun, and Chen Ying. "Research on Entity Relation Extraction of Chinese Medical Texts Based on Pre-Training Model." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10732945.

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Li, Xiuli, and Kai Yang. "Joint Entity and Relation Extraction Form Medical Information Based on Potential Relation and CasRel." In 2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC). IEEE, 2025. https://doi.org/10.1109/citsc64390.2025.00152.

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cheng, yingming, Dandan Zhao, and Jiana Meng. "Research on Chinese medical entity relationship extraction based on multineural network and syntactic information." In Fourth International Conference on Electronics Technology and Artificial Intelligence (ETAI 2025), edited by Shaohua Luo and Akash Saxena. SPIE, 2025. https://doi.org/10.1117/12.3068323.

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Ke, Yuanzhi, Zhangju Yin, Xinyun Wu, and Caiquan Xiong. "HBUT at #SMM4H 2024 Task2: Cross-lingual Few-shot Medical Entity Extraction using a Large Language Model." In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.smm4h-1.13.

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Li, Zehao, Ling Zhong, and Xinyi Han. "Research on the Entity Relationship Extraction Method of Large Model Chinese Electronic Medical Record With Low-Moment Features." In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2024. https://doi.org/10.1109/eiecs63941.2024.10800474.

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Zhang, Guobiao, Xueping Peng, Tao Shen, et al. "Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.810.

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Gupta, Anubhav. "Team Yseop at #SMM4H 2024: Multilingual Pharmacovigilance Named Entity Recognition and Relation Extraction." In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.smm4h-1.32.

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DENG, WEI, PANPAN GUO, and JIUDONG YANG. "Medical Entity Extraction and Knowledge Graph Construction." In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2019. http://dx.doi.org/10.1109/iccwamtip47768.2019.9067598.

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