Background: Health short videos, as an emerging mode of information dissemination, play a crucial role in enhancing health awareness and promoting healthy behavior among college students. It is crucial to optimize both the content and dissemination strategies of these videos to amplify their impact on health communication and to enhance the health literacy of this demographic.Objective: This study aims to construct a comprehensive model to explore the key factors influencing the use of health short videos among Chinese college students, including perceived usefulness, perceived ease of use, and performance expectations, among other factors. The goal is to supply theoretical foundations and practical guidance for optimizing health short video content and dissemination strategies, thereby enhancing college students’ health literacy and quality of life.Methods: An online survey was conducted among Chinese college students to investigate their inclination to acquire health information from health short videos. Based on TAM and UTAUT frameworks, a model was constructed to examine the factors influencing college students’ adoption of health information from health short videos. Through structural equation modeling, the study analyzed the impact of health short videos on the information adoption behavior of college students.Results: This study included a total of 296 Chinese college students. Results from the structural equation model indicated that perceived usefulness (β = 0.443, p < 0.001), perceived ease of use (β = 0.398, p < 0.001), performance expectancy (β = 0.434, p < 0.001), effort expectancy (β = 0.456, p < 0.001), social influence (β = 0.443, p < 0.001), information quality (β = 0.427, p < 0.001), and perceived trust (β = 0.482, p < 0.001) significantly positively influenced intention to adopt health short videos. Conversely, perceived risk (β = −0.415, p < 0.001) and perceived disease threat (β = −0.480, p < 0.001) had significant negative effects on adoption intention. Additionally, facilitating conditions (β = 0.421, p < 0.001) positively influenced adoption behavior, and adoption intention significantly affected adoption behavior (β = 0.406, p < 0.001).Conclusion: The adoption of health short videos by Chinese college students is primarily positively influenced by factors such as perceived usefulness, ease of use, performance expectancy, effort expectancy, social influence, information quality, and trust. Conversely, perceived risk and disease threat negatively affect their usage. Therefore, to promote college students’ continuous attention to and adoption of health short videos, the quality and credibility of health short videos should be improved, the user interface design should be optimized, usability should be enhanced, and social influence strategies should be used to enhance the attractiveness and persuasiveness of health information.

With the rapid development of Internet, the medical Q&A community has become an important channel for people to obtain and share medical and health knowledge. Online medical entity recognition (OMER), as the foundation of medical and health information extraction, has attracted extensive attention of researchers in recent years. In order to further improve the research progress of Chinese OMER, LSTM-Att-Med model is proposed in this paper to capture more external semantic features and important information. First, Word2vec is used to generate the character-level vectors with semantic features on the basis of the unlabeled corpus in the medical domain and open domain respectively. Then, the two character-level vectors are embedded into BiLSTM-CRF as features to construct LSTM-Wiki and LSTM-Med models. Finally, Self-Attention mechanism is introduced into LSTM-Med model, and the performance of the model is validated by using the self-labeled data. The 10-fold cross-validation experiment shows that LSTM-Att-Med with Self-Attention mechanism introduced achieves the best performance and the F-value can be up to 91.66%, which is 0.72% higher than that of BiLSTM-CRF. In addition, the experiment result demonstrates that the improvements of F-value are inconsistent for different corpora based on LSTM-Att-Med. The paper also analyzes the recognition performance and error results of different medical entities.

Background: With the rapid development of big data and artificial intelligence, the medical knowledge in medical literature has attracted more and more attention from the academic community. As the foundation for medical knowledge extraction and decision support system construction, Chinese medical literature named entity recognition (CMLNER) is the process of automatically recognizing entities in medical literature. Due to the diverse types, unclear boundary, complex composition and lack of explicit separators like space in Chinese medical entity, the task of CMLNER is more complicated compared with English medical literature named entity recognition. Objective: In this study, we aim to investigate novel methods to model CMLNER and analyze the results. Methods: This study proposes a novel neural network architectural model MFA-BERT-BiLSTM-CRF based on external medical knowledge and self-attention mechanism. Results: Compared with traditional NER methods, the proposed model could more effectively capture rich medical semantic features, global context information and further improve the performance of CMLNER. In addition, the key factors affecting CMLNER is also analyzed using the correlation coefficient method and the result indicates that the number and composition rules of entity are main factors. Finally, the recognition performance and error results are also analyzed in this paper. Conclusions: Our research makes up for the deficiency of previous frameworks and will further promote the development of medical entity recognition.