| Title: |
Precision in prevention and health surveillance:how artificial intelligence may improve the time of identification of health concerns through social media content analysis |
| Authors: |
Staccini, Pascal; Lau, Annie Y. S. |
| Source: |
Staccini, P & Lau, A Y S 2024, 'Precision in prevention and health surveillance : how artificial intelligence may improve the time of identification of health concerns through social media content analysis', Yearbook of Medical Informatics, vol. 33, no. 1, pp. 158-165. https://doi.org/10.1055/s-0044-1800736 |
| Publication Year: |
2024 |
| Subject Terms: |
Consumer Health Informatics; Social media; Health Surveillance; Mental Health; Artificial Intelligence; Machine Learning; Natural Language Processing; Public Health Informatics |
| Description: |
Objective: To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance "precision in prevention and health surveillance" (2024 Yearbook topic). The focus is on leveraging advanced data analytics to improve the timeliness and accuracy of identifying emerging health concerns, thus enabling more proactive and effective health interventions. Methods: A comprehensive literature search strategy was conducted on PubMed, focusing on papers published in 2023 related to consumer health informatics, precision prevention, and the intersection with social media. The search aimed to identify studies that utilized AI and machine learning techniques to analyse social media data for health surveillance purposes. Bibliometric analyses were applied to the retrieved articles, and tools such as "Bibliometrix" were used to assess keyword frequencies, co-occurrence networks, and thematic maps. The studies were then independently reviewed and screened for relevance, with a final selection of 10 articles made based on their alignment with the 2024 Yearbook topic and their methodological innovation. Results: The analysis of 89 articles revealed several key themes and findings. Social media data offers a rich source of real-time insights into public health trends, and encompasses diverse demographic groups. AI methodologies, including machine learning, natural language processing (NLP), and deep learning, play a crucial role in extracting and analysing health-related information from social media content. The integration of AI in health surveillance can provide early warnings of potential health crises, as demonstrated by studies on topics such as suicide prevention, mental health, and the impact of social media use on e-cigarette consumption among youth. Ethical and privacy considerations are paramount, necessitating robust data anonymization and transparent data handling practices. Advanced AI techniques, such as transformer-based topic modelling and federated ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
2364-0502 |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/40199301; info:eu-repo/semantics/altIdentifier/pissn/2364-0502; info:eu-repo/semantics/altIdentifier/eissn/2364-0502 |
| DOI: |
10.1055/s-0044-1800736 |
| Availability: |
https://researchers.mq.edu.au/en/publications/cb5f923e-4cad-453a-874f-0f68ee548519; https://doi.org/10.1055/s-0044-1800736; https://research-management.mq.edu.au/ws/files/428190337/Publisher_version_open_access_.pdf; https://www.scopus.com/pages/publications/105003189073 |
| Rights: |
info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.DCB28940 |
| Database: |
BASE |