Enabling Digital Health by Automatic Classification of Short Messages

TL;DR

This paper presents a hybrid system for real-time automatic classification of health-related SMS messages to assist UNICEF in managing increasing message volumes efficiently and accurately.

Contribution

The paper introduces a hybrid human-machine system for large-scale, high-speed classification of SMS messages in a health context, improving processing efficiency and accuracy.

Findings

System successfully classified messages in real-time during deployment.

Hybrid approach improved accuracy over purely automated methods.

Deployment in Zambia demonstrated practical effectiveness.

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.