Download PDF
Yearb Med Inform 2016; 25(01): 247-250
DOI: 10.15265/IY-2016-047
IMIA and Schattauer GmbH
Georg Thieme Verlag KG Stuttgart

Some Innovative Approaches for Public Health and Epidemiology Informatics

L. Toubiana
1   INSERM UMRS 1142 LIMICS, Université Pierre et Marie Curie - Paris, France
,
N. Griffon
1   INSERM UMRS 1142 LIMICS, Université Pierre et Marie Curie - Paris, France
2   Département d′information et d′informatique médicale, TIBS, LITIS EA 4108, Rouen University Hospital, Normandy, Rouen, France
,
Section Editors for the IMIA Yearbook Section on Public Health and Epidemiology Informatics › Author Affiliations
Further Information

Publication History

10 November 2016

Publication Date:
06 March 2018 (online)

   Permissions and Reprints

Summary

Objectives: Summarize excellent current research published in 2015 in the field of Public Health and Epidemiology Informatics.

Methods: The complete 2015 literature concerning public health and epidemiology informatics has been searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the editorial team an enlightened selection of the best papers.

Results: Among the 1,272 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one presents a language agnostic approach for epidemic event detection in news articles. The second paper describes a system using big health data gathered by a statewide system to forecast emergency department visits. The last paper proposes a rather original approach that uses machine learning to solve the old issue of outbreak detection and prediction.

Conclusions: The increasing availability of data, now directly from health systems, will probably lead to a boom in public health surveillance systems and in large-scale epidemiologic studies.

Keywords

Public health - epidemiology - medical informatics - International Medical Informatics Association - health information systems
 

  • References

  • 1 Petersen E, Wilson ME, Touch S, McCloskey B, Mwaba P, Bates M. et al. Rapid Spread of Zika Virus in The Americas - Implications for Public Health Preparedness for Mass Gatherings at the 2016 Brazil Olympic Games.. Int J Infect Dis 2016; Mar 44: 11-5.
    CrossrefPubMedGoogle Scholar
  • 2 Brunet J, Pfaff AW, Hansmann Y, Gregorowicz G, Pesson B, Abou-Bacar A. et al. An unusual case of hematuria in a French family returning from Corsica.. Int J Infect Dis 2015; Feb 31: 59-60.
    CrossrefPubMedGoogle Scholar
  • 3 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers.. Methods Inf Med 2015; 54 (Suppl. 02) 135-44.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 4 Ainsworth J, Buchan I. Combining Health Data Uses to Ignite Health System Learning.. Methods Inf Med 2015; 54: 479-87.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 5 Hu Z, Jin B, Shin AY, Zhu C, Zhao Y, Hao S. et al. Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study.. Interact J Med Res 2015; 4: e2.
    CrossrefPubMedGoogle Scholar
  • 6 Cooper GF, Villamarin R, Rich Tsui F-C, Millett N, Espino JU. et al. A method for detecting and characterizing outbreaks of infectious disease from clinical reports.. J Biomed Inform 2015; 53: 15-26.
    CrossrefPubMedGoogle Scholar
  • 7 Elliot AJ, Kara EO, Loveridge P, Bawa Z, Morbey RA, Moth M. et al. Internet-based remote health self-checker symptom data as an adjuvant to a national syndromic surveillance system.. Epidemiol Infect 2015; 143: 3416-22.
    CrossrefPubMedGoogle Scholar
  • 8 Ram S, Zhang W, Williams M, Pengetnze Y. Predicting asthma-related emergency department visits using big data.. IEEE J Biomed Health Inform 2015; 19: 1216-23.
    CrossrefPubMedGoogle Scholar
  • 9 Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.. PLoS Comput Biol 2015; 11: e1004513.
    CrossrefPubMedGoogle Scholar
  • 10 Lejeune G, Brixtel R, Doucet A, Lucas N. Multilingual event extraction for epidemic detection.. Artif Intell Med 2015; 65: 131-43.
    CrossrefPubMedGoogle Scholar
  • 11 Wang HW, Chen DR, Yu HW, Chen YM. Forecasting the Incidence of Dementia and Dementia-Related Outpatient Visits With Google Trends: Evidence From Taiwan.. J Med Internet Res 2015; Nov 19 17 (Suppl. 11) e264.
    CrossrefPubMedGoogle Scholar
  • 12 Jafarpour N, Izadi M, Precup D, Buckeridge DL. Quantifying the determinants of outbreak detection performance through simulation and machine learning.. J Biomed Inform 2015; 53: 180-7.
    CrossrefPubMedGoogle Scholar
  • 13 Lazard AJ, Scheinfeld E, Bernhardt JM, Wilcox GB, Suran M. Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention’s Ebola live Twitter chat.. Am J Infect Control 2015; 43: 1109-11.
    CrossrefPubMedGoogle Scholar
  • 14 Lister C, Royne M, Payne HE, Cannon B, Hanson C, Barnes M. The Laugh Model: Reframing and Rebranding Public Health Through Social Media.. Am J Public Health 2015; Nov 105 (Suppl. 11) 2245-51.
    CrossrefPubMedGoogle Scholar
  • 15 Barak-Corren Y, Reis BY. Internet activity as a proxy for vaccination compliance.. Vaccine 2015; May 15 33 (Suppl. 21) 2395-8.
    CrossrefPubMedGoogle Scholar
  • 16 Polo G, Acosta CM, Ferreira F, Dias RA. Location-allocation and accessibility models for improving the spatial planning of public health services.. PLoS One 2015; 10 (Suppl. 03) e0119190.
    CrossrefPubMedGoogle Scholar