Enabling digital health by automatic classification of short messages

Muhammad Imran, Patrick Meier, Carlos Castillo, Andre Lesa, Manuel Garcia Herranz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In response to the growing HIV/AIDS and other health-related issues, UNICEF through their U-Report platform receives thousands of messages (SMS) every day to pro-vide prevention strategies, health case advice, and counsel-ing support to vulnerable population. Due to a rapid in-crease in U-Report usage (up to 300% in last 3 years), plus approximately 1,000 new registrations each day, the volume of messages has thus continued to increase, which made it impossible for the team at UNICEF to process them in a timely manner. In this paper, we present a platform de-signed to perform automatic classification of short messages (SMS) in real-Time to help UNICEF categorize and prior-itize health-related messages as they arrive. We employ a hybrid approach, which combines human and machine intel-ligence that seeks to resolve the information overload issue by introducing processing of large-scale data at high-speed while maintaining a high classification accuracy. The sys-Tem has recently been tested in conjunction with UNICEF in Zambia to classify short messages received via the U-Report platform on various health related issues. The system is designed to enable UNICEF make sense of a large volume of short messages in a timely manner. In terms of evalua-Tion, we report design choices, challenges, and performance of the system observed during the deployment to validate its effectiveness.

Original languageEnglish
Title of host publicationDH 2016 - Proceedings of the 2016 Digital Health Conference
PublisherAssociation for Computing Machinery, Inc
Pages61-65
Number of pages5
ISBN (Print)9781450342247
DOIs
Publication statusPublished - 11 Apr 2016
Event6th International Conference on Digital Health, DH 2016 - Montreal, Canada
Duration: 11 Apr 201613 Apr 2016

Other

Other6th International Conference on Digital Health, DH 2016
CountryCanada
CityMontreal
Period11/4/1613/4/16

Fingerprint

United Nations
Health
Zambia
Vulnerable Populations
Acquired Immunodeficiency Syndrome
HIV
Processing

Keywords

  • Hybrid system
  • Short text classification
  • Stream processing
  • Supervised machine learning

ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications
  • Health Informatics

Cite this

Imran, M., Meier, P., Castillo, C., Lesa, A., & Herranz, M. G. (2016). Enabling digital health by automatic classification of short messages. In DH 2016 - Proceedings of the 2016 Digital Health Conference (pp. 61-65). Association for Computing Machinery, Inc. https://doi.org/10.1145/2896338.2896364

Enabling digital health by automatic classification of short messages. / Imran, Muhammad; Meier, Patrick; Castillo, Carlos; Lesa, Andre; Herranz, Manuel Garcia.

DH 2016 - Proceedings of the 2016 Digital Health Conference. Association for Computing Machinery, Inc, 2016. p. 61-65.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Imran, M, Meier, P, Castillo, C, Lesa, A & Herranz, MG 2016, Enabling digital health by automatic classification of short messages. in DH 2016 - Proceedings of the 2016 Digital Health Conference. Association for Computing Machinery, Inc, pp. 61-65, 6th International Conference on Digital Health, DH 2016, Montreal, Canada, 11/4/16. https://doi.org/10.1145/2896338.2896364
Imran M, Meier P, Castillo C, Lesa A, Herranz MG. Enabling digital health by automatic classification of short messages. In DH 2016 - Proceedings of the 2016 Digital Health Conference. Association for Computing Machinery, Inc. 2016. p. 61-65 https://doi.org/10.1145/2896338.2896364
Imran, Muhammad ; Meier, Patrick ; Castillo, Carlos ; Lesa, Andre ; Herranz, Manuel Garcia. / Enabling digital health by automatic classification of short messages. DH 2016 - Proceedings of the 2016 Digital Health Conference. Association for Computing Machinery, Inc, 2016. pp. 61-65
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