Abstract
Access to mobile phones in many low- and middle-income countries has increased exponentially over the last 20 years, providing an opportunity to connect patients with healthcare interventions through mobile phones (known as mobile health). A barrier to large-scale implementation of interactive mobile health interventions is the human effort needed to manage participant messages. In this study, we explore the use of natural language processing to improve healthcare workers' management of messages from pregnant and postpartum women in Kenya. Using multilingual, low-resource language text messages from the Mobile solutions for Women and Children's health (Mobile WACh NEO) study, we developed models to assess urgency of incoming messages. We evaluated models using a novel approach that focuses on clinical usefulness in either triaging or prioritizing messages. Our best-performing models did not reach the threshold for clinical usefulness we set, but have the potential to improve nurse workflow and responsiveness to urgent messages.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop |
| Editors | Yan Hanqi, Yang Zonghan, Sebastian Ruder, Wan Xiaojun |
| Place of Publication | Online |
| Publisher | Association for Computational Linguistics |
| Pages | 68-75 |
| Number of pages | 8 |
| ISBN (Print) | 9781955917568 |
| DOIs | |
| State | Published - Nov 2022 |
| Externally published | Yes |