TY - GEN
T1 - Enabling digital health by automatic classification of short messages
AU - Imran, Muhammad
AU - Meier, Patrick
AU - Castillo, Carlos
AU - Lesa, Andre
AU - Herranz, Manuel Garcia
PY - 2016/4/11
Y1 - 2016/4/11
N2 - 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.
AB - 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.
KW - Hybrid system
KW - Short text classification
KW - Stream processing
KW - Supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=84966526557&partnerID=8YFLogxK
U2 - 10.1145/2896338.2896364
DO - 10.1145/2896338.2896364
M3 - Conference contribution
AN - SCOPUS:84966526557
T3 - DH 2016 - Proceedings of the 2016 Digital Health Conference
SP - 61
EP - 65
BT - DH 2016 - Proceedings of the 2016 Digital Health Conference
PB - Association for Computing Machinery, Inc
T2 - 6th International Conference on Digital Health, DH 2016
Y2 - 11 April 2016 through 13 April 2016
ER -