Edited by Yanshan Wang, Hua Xu and Ozlem Uzuner.
Volume 19 Supplement 5
Selected articles from the second International Workshop on Health Natural Language Processing (HealthNLP 2019)
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. YH and HX were not involved in the decision making process of papers they had co-authored or collaborated with and the peer review was handled by one of the other Supplement Editors. No other competing interests were declared.
Xi'an, China10 June 2019
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Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):233
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Natural language processing for populating lung cancer clinical research data
Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics a...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):239 -
TestIME: an application for evaluating the efficiency of Chinese input method engines in electronic medical record entry task
With the wide application of Electronic Medical Record (EMR) systems, it has become a daily work for doctors using keyboards to input clinical information into the EMR system. Chinese Input Method Engine (IME)...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):237 -
An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records
Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):235 -
Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer’s disease
Extracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful rela...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):240 -
A study of deep learning methods for de-identification of clinical notes in cross-institute settings
De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):232 -
RCorp: a resource for chemical disease semantic extraction in Chinese
To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning ...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):234 -
Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text
To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):236 -
Improving rare disease classification using imperfect knowledge graph
Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the ...
Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 5):238
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2022 Citation Impact
3.5 - 2-year Impact Factor
3.9 - 5-year Impact Factor
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