Selected articles from the second International Workshop on Health Natural Language Processing (HealthNLP 2019)

Editorial: The second international workshop on health natural language processing (HealthNLP 2019)

Authors: Yanshan Wang, Hua Xu and Ozlem Uzuner

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...

Authors: Liwei Wang, Lei Luo, Yanshan Wang, Jason Wampfler, Ping Yang and Hongfang Liu

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)...

Authors: Feihong Yang, Haihong Guo and Jiao Li

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...

Authors: Luqi Li, Jie Zhao, Li Hou, Yunkai Zhai, Jinming Shi and Fangfang Cui

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...

Authors: Go Eun Heo, Qing Xie, Min Song and Jeong-Hoon Lee

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...

Authors: Xi Yang, Tianchen Lyu, Qian Li, Chih-Yin Lee, Jiang Bian, William R. Hogan and Yonghui Wu

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 ...

Authors: Yueping Sun, Li Hou, Lu Qin, Yan Liu, Jiao Li and Qing Qian

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...

Authors: Jun Xu, Zhiheng Li, Qiang Wei, Yonghui Wu, Yang Xiang, Hee-Jin Lee, Yaoyun Zhang, Stephen Wu and Hua Xu

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 ...

Authors: Xuedong Li, Yue Wang, Dongwu Wang, Walter Yuan, Dezhong Peng and Qiaozhu Mei