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- Publisher Website: 10.1016/j.idh.2018.10.002
- Scopus: eid_2-s2.0-85055753611
- PMID: 30541697
- WOS: WOS:000456933900006
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Article: Artificial Intelligence for infectious disease Big Data Analytics
| Title | Artificial Intelligence for infectious disease Big Data Analytics |
|---|---|
| Authors | |
| Keywords | Artificial Intelligence Emergency response Infectious diseases modelling Machine learning |
| Issue Date | 2019 |
| Citation | Infection, Disease and Health, 2019, v. 24, n. 1, p. 44-48 How to Cite? |
| Abstract | Background: Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the advancement of information and communications technology and the data collection systems now in place. Methods: This paper aims to highlight the opportunities gained through the use of Artificial Intelligence (AI) methods to enable reliable disease-oriented monitoring and projection in this information age. Results and Conclusion: It is foreseeable that together with reliable data management platforms AI methods will enable analysis of massive infectious disease and surveillance data effectively to support government agencies, healthcare service providers, and medical professionals to response to disease in the future. |
| Persistent Identifier | http://hdl.handle.net/10722/330587 |
| ISSN | 2023 Impact Factor: 2.7 2023 SCImago Journal Rankings: 0.738 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Wong, Zoie S.Y. | - |
| dc.contributor.author | Zhou, Jiaqi | - |
| dc.contributor.author | Zhang, Qingpeng | - |
| dc.date.accessioned | 2023-09-05T12:12:02Z | - |
| dc.date.available | 2023-09-05T12:12:02Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Infection, Disease and Health, 2019, v. 24, n. 1, p. 44-48 | - |
| dc.identifier.issn | 2468-0451 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/330587 | - |
| dc.description.abstract | Background: Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the advancement of information and communications technology and the data collection systems now in place. Methods: This paper aims to highlight the opportunities gained through the use of Artificial Intelligence (AI) methods to enable reliable disease-oriented monitoring and projection in this information age. Results and Conclusion: It is foreseeable that together with reliable data management platforms AI methods will enable analysis of massive infectious disease and surveillance data effectively to support government agencies, healthcare service providers, and medical professionals to response to disease in the future. | - |
| dc.language | eng | - |
| dc.relation.ispartof | Infection, Disease and Health | - |
| dc.subject | Artificial Intelligence | - |
| dc.subject | Emergency response | - |
| dc.subject | Infectious diseases modelling | - |
| dc.subject | Machine learning | - |
| dc.title | Artificial Intelligence for infectious disease Big Data Analytics | - |
| dc.type | Article | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1016/j.idh.2018.10.002 | - |
| dc.identifier.pmid | 30541697 | - |
| dc.identifier.scopus | eid_2-s2.0-85055753611 | - |
| dc.identifier.volume | 24 | - |
| dc.identifier.issue | 1 | - |
| dc.identifier.spage | 44 | - |
| dc.identifier.epage | 48 | - |
| dc.identifier.eissn | 2468-0869 | - |
| dc.identifier.isi | WOS:000456933900006 | - |