Health Natural Language Processing: Methodology Development and Applications

With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and e...

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Vydáno v:JMIR medical informatics Ročník 9; číslo 10; s. e23898
Hlavní autoři: Hao, Tianyong, Huang, Zhengxing, Liang, Likeng, Weng, Heng, Tang, Buzhou
Médium: Journal Article
Jazyk:angličtina
Vydáno: Toronto JMIR Publications 21.10.2021
Témata:
Artificial intelligence
Clinical trials
Datasets
Decision making
Deep learning
Electronic health records
Fraud
Health informatics
Knowledge representation
Language
Lung cancer
Machine learning
Medical records
Medical research
Natural language processing
Neural networks
Semantics
Social networks
Subject specialists
Traditional Chinese medicine
China
ISSN:2291-9694, 2291-9694
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Abstract With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".
AbstractList With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".
With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".
Author Huang, Zhengxing
Weng, Heng
Hao, Tianyong
Liang, Likeng
Tang, Buzhou
AuthorAffiliation 2 College of Computer Science and Technology Zhejiang University Guangzhou China
1 School of Computer Science South China Normal University Guangzhou China
4 School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China
3 The Second Affiliated Hospital Guangzhou University of Chinese Medicine Guangzhou China
AuthorAffiliation_xml – name: 3 The Second Affiliated Hospital Guangzhou University of Chinese Medicine Guangzhou China
– name: 1 School of Computer Science South China Normal University Guangzhou China
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– name: 4 School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China
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Cites_doi 10.1016/j.jbi.2019.103323
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ContentType Journal Article
Copyright 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2021.
Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2021. 2021
Copyright_xml – notice: 2021. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2021.
– notice: Tianyong Hao, Zhengxing Huang, Likeng Liang, Heng Weng, Buzhou Tang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2021. 2021
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Clinical trials
Datasets
Decision making
Deep learning
Electronic health records
Fraud
Health informatics
Knowledge representation
Language
Lung cancer
Machine learning
Medical records
Medical research
Natural language processing
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Semantics
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