Skip to main content Accessibility help
×
Home

Prediction of Shigellosis outcomes in Israel using machine learning classifiers

  • G. Adamker (a1), T. Holzer (a1), I. Karakis (a2) (a3), M. Amitay (a1), E. Anis (a2), S. R. Singer (a2) and Z. Barnett-Itzhaki (a1) (a2)...

Abstract

Shigellosis causes significant morbidity and mortality in developing and developed countries, mostly among infants and young children. The World Health Organization estimates that more than one million people die from Shigellosis every year. In order to evaluate trends in Shigellosis in Israel in the years 2002–2015, we analysed national notifiable disease reporting data. Shigella sonnei was the most commonly identified Shigella species in Israel. Hospitalisation rates due to Shigella flexenri were higher in comparison with other Shigella species. Shigella morbidity was higher among infants and young children (age 0–5 years old). Incidence of Shigella species differed among various ethnic groups, with significantly high rates of S. flexenri among Muslims, in comparison with Jews, Druze and Christians. In order to improve the current Shigellosis clinical diagnosis, we developed machine learning algorithms to predict the Shigella species and whether a patient will be hospitalised or not, based on available demographic and clinical data. The algorithms’ performances yielded an accuracy of 93.2% (Shigella species) and 94.9% (hospitalisation) and may consequently improve the diagnosis and treatment of the disease.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Prediction of Shigellosis outcomes in Israel using machine learning classifiers
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Prediction of Shigellosis outcomes in Israel using machine learning classifiers
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Prediction of Shigellosis outcomes in Israel using machine learning classifiers
      Available formats
      ×

Copyright

Corresponding author

Author for correspondence: Z. Barnett-Itzhaki, E-mail: zohar.barnett@moh.gov.il

Footnotes

Hide All
*

These authors contributed equally to this work.

Footnotes

References

Hide All
1.Yang, J et al. (2005) Genome dynamics and diversity of Shigella species, the etiologic agents of bacillary dysentery. Nucleic Acids Research 33, 64456458.
2.CDC. Shigella – Shigellosis. Available at https://www.cdc.gov/shigella/index.html (Accessed 14 February 2018).
3.Ansaruzzaman, M et al. (2001) Epidemiology of postshigellosis persistent diarrhea in young children. Pediatric Infectious Disease Journal 20, 525530.
4.World Health Organization. Guidelines for the control of shigellosis, including epidemics due to Shigella. Available at http://apps.who.int/iris/bitstream/10665/43252/1/924159330X.pdf (Accessed 14 February 2018).
5.Iwamoto, M et al. (2014) Incidence and trends of infection with pathogens transmitted commonly through food–foodborne diseases active surveillance network, 10 U.S. Sites, 2006–2013. MMWR Morbidity and Mortality Weekly Report 63, 328332.
6.Block, C et al. (1991) Four decades of shigellosis in Israel: epidemiology of a growing public health problem. Reviews of Infectious Diseases 13, 248253.
7.Bassal, R et al. (2014) Recent trends in the epidemiology of shigellosis in Israel. Epidemiology and Infection 142, 25832594.
8.GBD Diarrhoeal Diseases Collaborators (2017) Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Infectious Diseases 17, 909948.
9.Strina, A et al. (2008) A hierarchical model for studying risk factors for childhood Diarrhoea: a case-control study in a middle-income country. International Journal of Epidemiology 37, 805815.
10.Smith, MA et al. (1999) Comparison of six dose-response models for use with food-borne pathogens. Risk Analysis 19, 10911100.
11.Arevalillo, JM et al. (2017) Identification of immune correlates of protection in Shigella infection by application of machine learning. Journal of Biomedical Informatics 74, 19.
12.Israeli Central Bureau of Statistics. 8.7 M residents in the state of Israel. Available at http://www.cbs.gov.il/reader/newhodaot/hodaa_template.html?hodaa=201711113 (Accessed 14 February 2018).
13.Israeli Central Bureau of Statistics. Population by religion. Available at http://www.cbs.gov.il/shnaton68/st02_02.pdf (Accessed 14 February 2018).
14.Israeli Bureau of Statistics. Bureau of Statistics. Available at http://www.cbs.gov.il (Accessed 14 January 2018).
15.Harrison, RF and Kennedy, RL (1995) Cross SS, Introduction to neural networks. Lancet 346, 10751079.
16.Noble, WS (2006) What is a support vector machine? Nature Biotechnology 24, 15651567.
17.Polyak, CS et al. (2004) Laboratory-confirmed shigellosis in the United States, 1989–2002: epidemiologic trends and patterns. Clinical Infectious Diseases 38, 13721377.
18.Israeli Ministry of Health. Notifiable infectious diseases in Iseael. 60 years of surveillance 1951–2010. 2012. Available at https://www.health.gov.il/PublicationsFiles/Disease1951_2010.pdf (Accessed January 2018).

Keywords

Type Description Title
WORD
Supplementary materials

Adamker et al. supplementary material
Adamker et al. supplementary material 1

 Word (146 KB)
146 KB

Prediction of Shigellosis outcomes in Israel using machine learning classifiers

  • G. Adamker (a1), T. Holzer (a1), I. Karakis (a2) (a3), M. Amitay (a1), E. Anis (a2), S. R. Singer (a2) and Z. Barnett-Itzhaki (a1) (a2)...

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed