Enabling Digital Health by Automatic Classification of Short Messages

Muhammad Imran, Patrick Meier, Carlos Castillo, Andre Lesa, Manuel Garcia Herranz
2016 Proceedings of the 6th International Conference on Digital Health Conference - DH '16  
In response to the growing HIV/AIDS and other healthrelated issues, UNICEF through their U-Report platform receives thousands of messages (SMS) every day to provide prevention strategies, health case advice, and counseling 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
more » ... 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.
doi:10.1145/2896338.2896364 dblp:conf/ehealth/ImranMCLH16 fatcat:fwvegc6mprfyvmkb4pxasrjdzq