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arXiv · 1602.08423

Enabling Digital Health by Automatic Classification of Short Messages

Muhammad Imran·Patrick Meier·Carlos Castillo·Andre Lesa·Manuel Garcia Herranz
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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 provide prevention strategies, health case advice, and counsel- ing support to vulnerable population. Due to a rapid increase 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 designed to perform automatic classification of short messages (SMS) in real-time to help UNICEF categorize and prioritize health-related messages as they arrive. We employ a hybrid approach, which combines human and machine intelligence 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 system 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 evaluation, we report design choices, challenges, and performance of the system observed during the deployment to validate its effectiveness.

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Muhammad Imran, Patrick Meier, Carlos Castillo, Andre Lesa, Manuel Garcia Herranz. 2016-02-26. Enabling Digital Health by Automatic Classification of Short Messages. https://arxiv.org/abs/1602.08423

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