Enhancing Maternal Healthcare: Training Language Models to Identify Urgent Messages in Real-Time
We have fine-tuned the Gemma-2 2-billion parameter instruction model on a custom dataset in order to detect whether user messages pertain to urgent or non-urgent maternal healthcare issues. Our model demonstrates superior performance compared to GPT-3.5-Turbo in accurately distinguishing between urgent and non-urgent messages. Both the dataset and the model have been made publicly available to support further research and development in this critical area.1
Maternal health support is critical for the well-being of mothers and the health of their children. Addressing maternal health issues reduces mortality rates and promotes equity, ensuring that all women have access to the care they need, regardless of their circumstances. Ensuring access to maternal healthcare also plays a pivotal role in economic stability by enabling mothers to effectively participate in the workforce and care for their families.
MomConnect, a South African government initiative, has been a pioneer in using mobile technology to connect pregnant women and new mothers with essential healthcare information and services. By leveraging mobile phones, the initiative has been able to reach millions of women across South Africa, particularly in remote and underserved areas. In particular, MomConnect’s help desk serves as a vital resource for pregnant women and mothers with infants by providing real-time support for maternal health inquiries. Due to the high volume of messages being sent to the help desk, it is essential to prioritize urgent messages to ensure that critical needs are met without delay.
