Edited by V.G.Vinod Vydiswaran, Hua Xu, Yaoyun Zhang and Yanshan Wang.
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
New Content Item
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Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):76
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A two-site survey of medical center personnel’s willingness to share clinical data for research: implications for reproducible health NLP research
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):70 -
Discovering associations between problem list and practice setting
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):69 -
Natural language processing of radiology reports for identification of skeletal site-specific fractures
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):73 -
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
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 ...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):71 -
Identifying peer experts in online health forums
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 ...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):68 -
Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):77 -
Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):75 -
Extracting health-related causality from twitter messages using natural language processing
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):79 -
Developing a portable natural language processing based phenotyping system
This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches.
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):78 -
Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):74 -
Facilitating accurate health provider directories using natural language processing
Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintain...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):80 -
EHR problem list clustering for improved topic-space navigation
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...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 3):72
Annual Journal Metrics
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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)2022 Speed
28 days submission to first editorial decision for all manuscripts (Median)
170 days submission to accept (Median)2022 Usage
2,263,856 downloads
4,290 Altmetric mentions
Peer-review Terminology
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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