Manuscript received March 11, 2024; revised June 3, 2024; accepted June 26, 2024; published August 28, 2024
Abstract—The advances in mobile technology and natural
language processing have made chatbots suitable for personal
health care management. When the world’s population is aging,
diabetes becomes one of the most common chronic diseases in
the world. After being discharged from a hospital, diabetes
patients must conduct personal health care management such
as monitoring blood glucose, professional diet advice, and
regular exercise reminders to control the disease. Unfortunately,
it has been found that there are few chatbots designated for
diabetes patients, especially in the Chinese language. To fulfill
the need, this research proposes a diabetes self-management
chatbot to assist patients in recording their blood glucose,
exercise, and diet through conversation. The proposed chatbot
system consists of four main components: a dialog controller, a
neural network, a personal database, and a diabetic
management rule base. Iterated Dilated Convolutional Neural
Network with Conditional Random Field (ID-CNN-CRF) is
applied for Named Entity Recognition (NER) to achieve high
chatting quality. The experiments show that the ID-CNN-CRF
outperforms the other three popular CNN, LSTM, and Bi-
LSTM-CRF models regarding intention prediction accuracy
and response time. In addition, the chatbot can advise people
with diabetes on proper diet and exercise. The feedback from
the diabetes caregivers and patients shows that the proposed
chatbot is recognized as a convenient self-management tool.
Keywords—diabetes, self-management, chatbot, neural
networks
Cite: Chieh-Yuan Tsai, Hen-Yi Jen, and Tai-Jung Chiang, "A Neural Network-based Diabetes Self-Management Chatbot System," International Journal of Machine Learning vol. 14, no. 3, pp. 84-91, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).