Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling quer...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7114 |
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| author | Yongxiang Zhang Raymond Y. K. Lau |
| author_facet | Yongxiang Zhang Raymond Y. K. Lau |
| author_sort | Yongxiang Zhang |
| collection | DOAJ |
| description | Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling queries with unknown intents due to the technical bottleneck and restricted user-intent answering scope. Furthermore, the wide variation in a user’s consultation needs and levels of medical knowledge further complicates the chatbot’s ability to understand natural human language. Failure to deal with unknown intents may lead to a significant risk of incorrect information acquisition. In this study, we develop an unknown intent detection model to facilitate chatbots’ decisions in responding to uncertain queries. Our work focuses on algorithmic innovation for high-risk healthcare scenarios, where asymmetric knowledge between patients and experts exacerbates intent recognition challenges. Given the multi-role context, we propose a novel query representation learning approach involving multiple views from chatbot users, medical experts, and system developers. Unknown intent detection is then accomplished through the transformed representation of each query, leveraging adaptive determination of intent decision boundaries. We conducted laboratory-level experiments and empirically validated the proposed method based on the real-world user query data from the Tianchi lab and medical information from the Xunyiwenyao website. Across all tested unknown intent ratios (25%, 50%, and 75%), our multi-view boundary learning method was proven to outperform all benchmark models on the metrics of accuracy score, macro F1-score, and macro F1-scores over known intent classes and over the unknown intent class. |
| format | Article |
| id | doaj-art-254d0e9729054c04a489c078d693e2f8 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-254d0e9729054c04a489c078d693e2f82025-08-20T03:16:43ZengMDPI AGApplied Sciences2076-34172025-06-011513711410.3390/app15137114Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown IntentsYongxiang Zhang0Raymond Y. K. Lau1Department of Information Systems, City University of Hong Kong, Hong Kong SAR, ChinaDepartment of Information Systems, City University of Hong Kong, Hong Kong SAR, ChinaChatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling queries with unknown intents due to the technical bottleneck and restricted user-intent answering scope. Furthermore, the wide variation in a user’s consultation needs and levels of medical knowledge further complicates the chatbot’s ability to understand natural human language. Failure to deal with unknown intents may lead to a significant risk of incorrect information acquisition. In this study, we develop an unknown intent detection model to facilitate chatbots’ decisions in responding to uncertain queries. Our work focuses on algorithmic innovation for high-risk healthcare scenarios, where asymmetric knowledge between patients and experts exacerbates intent recognition challenges. Given the multi-role context, we propose a novel query representation learning approach involving multiple views from chatbot users, medical experts, and system developers. Unknown intent detection is then accomplished through the transformed representation of each query, leveraging adaptive determination of intent decision boundaries. We conducted laboratory-level experiments and empirically validated the proposed method based on the real-world user query data from the Tianchi lab and medical information from the Xunyiwenyao website. Across all tested unknown intent ratios (25%, 50%, and 75%), our multi-view boundary learning method was proven to outperform all benchmark models on the metrics of accuracy score, macro F1-score, and macro F1-scores over known intent classes and over the unknown intent class.https://www.mdpi.com/2076-3417/15/13/7114healthcare chatbotintent detectionmulti-view learningdecision boundaryrepresentation learning |
| spellingShingle | Yongxiang Zhang Raymond Y. K. Lau Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents Applied Sciences healthcare chatbot intent detection multi-view learning decision boundary representation learning |
| title | Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents |
| title_full | Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents |
| title_fullStr | Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents |
| title_full_unstemmed | Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents |
| title_short | Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents |
| title_sort | uncertainty detection a multi view decision boundary approach against healthcare unknown intents |
| topic | healthcare chatbot intent detection multi-view learning decision boundary representation learning |
| url | https://www.mdpi.com/2076-3417/15/13/7114 |
| work_keys_str_mv | AT yongxiangzhang uncertaintydetectionamultiviewdecisionboundaryapproachagainsthealthcareunknownintents AT raymondyklau uncertaintydetectionamultiviewdecisionboundaryapproachagainsthealthcareunknownintents |