A5011255280- Biomedical Text Mining and Ontologies
- Topic Modeling
- Machine Learning in Healthcare
- Natural Language Processing Techniques
- AI in cancer detection
- Tourism, Volunteerism, and Development
- Hospitality and Tourism Education
- Educational Systems and Policies
- Recreation, Leisure, Wilderness Management
- Health Literacy and Information Accessibility
- Corporate Finance and Governance
- Data Quality and Management
- Digital Marketing and Social Media
- Aquaculture Nutrition and Growth
- Social Media in Health Education
- Thyroid Disorders and Treatments
- Medical Coding and Health Information
- Data Stream Mining Techniques
- Sport and Mega-Event Impacts
- Memory and Neural Mechanisms
- Telemedicine and Telehealth Implementation
- Computational Drug Discovery Methods
- Aquatic life and conservation
- Autoimmune Neurological Disorders and Treatments
- Artificial Intelligence in Healthcare
Qingdao Center of Resource Chemistry and New Materials
2024
Jinan University
2021-2022
Shanghai Mental Health Center
2022
Shanghai Jiao Tong University
2022
The University of Texas Health Science Center at Houston
2018-2021
Mayo Clinic in Florida
2020
Columbia University
2017
Sun Yat-sen University
2015
Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more embedding methods and (e.g., ELMo, BERT) further pushed the state-of-the-art in NLP, yet there are no common best practices for how to integrate these into tasks. The purpose of this study, then, is explore space possible options utilizing new models extraction, comparing traditional word...
Sports tourism is an emerging product. In the sports and industry, resource mining foundation that provides positive significance for theoretical support. This study takes China’s boutique projects as object, exploring its spatial distribution pattern through average nearest neighbor index, kernel density, autocorrelation. On strength of wuli–shili–renli system approach, entropy value method geographic detector probe model are used to identify driving factors affecting pattern. Findings...
Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression rare but severe adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition extract nervous system disorder-related from safety reports.We collected Guillain-Barré syndrome (GBS) related influenza Vaccine Adverse Event Reporting System (VAERS) 1990 2016. VAERS were selected manually annotated with major...
Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along longitudinal sequence a patient's records, which may lead to incorrect selection contexts. To address this issue, we extended three popular concept embedding methods: word2vec, positive pointwise mutual information (PPMI) FastText, time-sensitive...
Eligibility criteria are important for clinical research protocols or practice guidelines determining who qualify studies and to whom evidence is applicable, but the free-text format not amenable computational processing. In this paper, we described a practical method transforming eligibility of Alzheimer's trials into structured relational database compliant with standards medical terminologies data models. We utilized hybrid natural language processing system concept normalization tool...
Rural homestay inns are an important part of rural tourism, and tourists' support behavior intentions factors affecting whether can be developed sustainably. The local authentic life experiences realization actual communication between the host tourists main influencing for to revisit, recommend, or provide support. Although previous studies have confirmed influence authenticity perception on from different perspectives, they not analyzed mechanism them perspective micro interpersonal...
The COVID-19 pandemic resulted in a large expansion of telehealth, but little is known about user sentiment. Tweets containing the terms “telehealth” and “telemedicine” were extracted (n = 192,430) from official Twitter API between November 2019 April 2020. A random subset 2000 tweets was annotated by trained readers to classify according their content, including sentiment, type, relation COVID-19. state-of-the-art NLP model (Bidirectional Encoder Representations Transformers, BERT) used...
Due to the epidemic war and other causes, global economic development effect is not good situation, China's economy still maintains a strong momentum, so many countries for appreciation of renminbi began sound, improve exchange rate which undoubtedly very favorable situation our country, this article would like think that motivation impact analysis summary generalization elaboration.
We present a Three-level Hierarchical Transformer Network (3-level-HTN) for modeling long-term dependencies across clinical notes the purpose of patient-level prediction. The network is equipped with three levels Transformer-based encoders to learn progressively from words sentences, sentences notes, and finally patients. first level word sentence directly applies pre-trained BERT model as fully trainable component. While second third both implement stack transformer-based encoders, before...
This paper describes an initial dataset and automatic natural language processing (NLP) method for extracting concepts related to precision oncology from biomedical research articles. We extract five concept types: Cancer, Mutation, Population, Treatment, Outcome. A corpus of 250 abstracts were annotated with these following standard double-annotation procedures. then experiment BERT-based models extraction. The best-performing model achieved a 63.8%, recall 71.9%, F1 67.1. Finally, we...