The circulation of numerous health care rumors on the internet in recent years has become extremely troublesome for medical and healthcare personnel. False rumors have misled patients, created panic, and even resulted in medication misuse and delayed treatment. They may also cause deteriorating doctor-patient relationships and tarnish the images of doctors and hospitals. Developing new mechanisms to dispel and prevent rumors has thus become an imperative issue in research. Although medical institutions promote accurate health education, limited labor and resources limit their effectiveness. The rapid progress in artificial intelligence technology is offering a new direction to solve this problem. This project aims to apply deep learning technologies, BERT (Bidirectional Encoder Representations from Transformers), to develop a new-generation health care rumor recognition system mainly functioning to assess food and drug rumors and provide personalized health information (e.g., information on medication, therapy, diet, and exercise). This project will be divided into two phases. In Phase 1, we will use artificial intelligence technology to construct a health care rumor recognition system. System design and testing will be based on the prevention and health education. In Phase 2, we will employ the Technology Acceptance Model to examine user intention to use the health care rumor recognition system. The achievements of this project will help to alleviate the impact of false rumors on patients.
|Effective start/end date||1/08/20 → 31/12/21|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Health education
- Intelligent information system
- Adoption intention
- Technology Acceptance Model
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