Ailbot 2.0: A Symptom Checker Chatbot for Skin Diseases of Young Children
Filipinos have faced multiple challenges in managing certain diseases during the pandemic. Specifically, skin diseases in Filipino children and adolescents were left uncared for during this period, which can drastically reduce a child’s quality of life if left alone. Thus, symptom checker chatbots m...
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| Main Authors: | , , , |
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| Format: | text |
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Animo Repository
2023
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/conf_shsrescon/2023/paper_csr/7 https://animorepository.dlsu.edu.ph/context/conf_shsrescon/article/1832/viewcontent/Ailbot_2.0__A_Symptom_Checker_Chatbot_for_Skin_Diseases_of_Young_Children.docx.pdf |
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| Institution: | De La Salle University |
| Summary: | Filipinos have faced multiple challenges in managing certain diseases during the pandemic. Specifically, skin diseases in Filipino children and adolescents were left uncared for during this period, which can drastically reduce a child’s quality of life if left alone. Thus, symptom checker chatbots may be integrated into various platforms, which can provide proper medication and skin care advice, specifically to young Filipino children. This study presents a symptom checker chatbot that attempts to assist young children in providing clinical impressions regarding specific conditions. The chatbot systematically asks for the symptoms of a child as the child answers yes/no to the questions until the chatbot finally generates the impression on what could possibly be the skin disease that the child may be experiencing. Since the target users are children, the questions of the chatbot were designed to be directed, simple and had included some images to easily understand the symptoms being asked. Sixteen Filipino children aged 8-12 years old had tested the chatbot and answered some questions on the system usability and user experience. Results had shown that the System Usability Scale average score is 85.63% indicating general usability while the mean Functionality score and the mean Performance score are both 98% suggesting that the chatbot had shown good performance and functionality during conversations. |
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