Research Article
eDEM-CONNECT: An Ontology-Based Chatbot for Family Caregivers of People with Dementia
@INPROCEEDINGS{10.1007/978-3-031-60665-6_6, author={Maurice Boiting and Niklas Tschorn and Sumaiya Suravee and Kristina Yordanova and Margareta Halek and Franziska A. Jagoda and Stefan L\'{y}dtke and Anja Burmann}, title={eDEM-CONNECT: An Ontology-Based Chatbot for Family Caregivers of People with Dementia}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={chatbot ontology home care natural language processing transformer models Markov logic network}, doi={10.1007/978-3-031-60665-6_6} }- Maurice Boiting
Niklas Tschorn
Sumaiya Suravee
Kristina Yordanova
Margareta Halek
Franziska A. Jagoda
Stefan Lüdtke
Anja Burmann
Year: 2024
eDEM-CONNECT: An Ontology-Based Chatbot for Family Caregivers of People with Dementia
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_6
Abstract
Home care of people with dementia (PwD) is mainly organized and carried out by non-professional family caregivers, who struggle to interpret the needs of PwD correctly and are confronted with the challenging behavior of their relatives. Although support services for family caregivers are widespread in Germany, they are rarely used due to the fact that information is poorly organized and relatives are faced with a flood of disorganized, outdated, and confusing content. Due to the technical development of chatbot technologies (ChatGPT), chatbots gain more and more relevance. Based on the new technological possibilities, we developed an online communication and service platform with an integrated chatbot within theeDEM-CONNECTproject, with the aim of making structured and easily understandable information accessible for family caregivers. This work focuses on the development of a chatbot pipeline that has broad domain knowledge through a provided ontology on the topic of agitation of PwD. This allows the chatbot to provide relevant and peer-reviewed information to family members. In our approach, a patient history is first taken based on several diagnostic questions so that relevant information can be output in a later step. For this purpose, we demonstrate that agitations in natural language can be correctly recognized by the usedBERT modeland that our developed chatbot is able to select further diagnostic questions based on the predictions of aMarkov logic network.
