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Volume 19 Supplement 3

Selected articles from the first International Workshop on Health Natural Language Processing (HealthNLP 2018)

Research

Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. VV was co-author of two papers in the supplement, the peer review of these papers were managed by HX and YZ. HX and YZ were co-authors of a paper in the supplement, the peer review was managed by YW. YW was co-author of two papers in the supplement, the peer review of these papers were managed by VV. No other competing interests were declared.

New York, NY, USA4-7 June 2018

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Conference website

Edited by V.G.Vinod Vydiswaran, Hua Xu, Yaoyun Zhang and Yanshan Wang.

  1. A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes...

    Authors: Chunhua Weng, Carol Friedman, Casey A. Rommel and John F. Hurdle
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):70
  2. The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patien...

    Authors: Liwei Wang, Yanshan Wang, Feichen Shen, Majid Rastegar-Mojarad and Hongfang Liu
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):69
  3. Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveill...

    Authors: Yanshan Wang, Saeed Mehrabi, Sunghwan Sohn, Elizabeth J. Atkinson, Shreyasee Amin and Hongfang Liu
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):73
  4. Clinical text classification is an fundamental problem in medical natural language processing. Existing studies have cocnventionally focused on rules or knowledge sources-based feature engineering, but only a ...

    Authors: Liang Yao, Chengsheng Mao and Yuan Luo
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):71
  5. Online health forums have become increasingly popular over the past several years. They provide members with a platform to network with peers and share information, experiential advice, and support. Among the ...

    Authors: V.G.Vinod Vydiswaran and Manoj Reddy
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):68
  6. A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes...

    Authors: Yaoyun Zhang, Firat Tiryaki, Min Jiang and Hua Xu
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):77
  7. Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identif...

    Authors: David A. Hanauer, Qiaozhu Mei, V. G. Vinod Vydiswaran, Karandeep Singh, Zach Landis-Lewis and Chunhua Weng
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):75
  8. Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encou...

    Authors: Son Doan, Elly W. Yang, Sameer S. Tilak, Peter W. Li, Daniel S. Zisook and Manabu Torii
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):79
  9. This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches.

    Authors: Himanshu Sharma, Chengsheng Mao, Yizhen Zhang, Haleh Vatani, Liang Yao, Yizhen Zhong, Luke Rasmussen, Guoqian Jiang, Jyotishman Pathak and Yuan Luo
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):78
  10. Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather...

    Authors: Buzhou Tang, Xiaolong Wang, Jun Yan and Qingcai Chen
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):74
  11. The amount of patient-related information within clinical information systems accumulates over time, especially in cases where patients suffer from chronic diseases with many hospitalizations and consultations...

    Authors: Markus Kreuzthaler, Bastian Pfeifer, Jose Antonio Vera Ramos, Diether Kramer, Victor Grogger, Sylvia Bredenfeldt, Markus Pedevilla, Peter Krisper and Stefan Schulz
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):72

Annual Journal Metrics

  • 2022 Citation Impact
    3.5 - 2-year Impact Factor
    3.9 - 5-year Impact Factor
    1.384 - SNIP (Source Normalized Impact per Paper)
    0.940 - SJR (SCImago Journal Rank)

    2023 Speed
    37 days submission to first editorial decision for all manuscripts (Median)
    213 days submission to accept (Median)

    2023 Usage 
    2,588,758 downloads
    2,443 Altmetric mentions 

Peer-review Terminology

  • The following summary describes the peer review process for this journal:

    Identity transparency: Single anonymized

    Reviewer interacts with: Editor

    Review information published: Review reports. Reviewer Identities reviewer opt in. Author/reviewer communication

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