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  4. Interactive Healthcare Robot using Attention-based Question-Answer Retrieval and Medical Entity Extraction Models
 
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Interactive Healthcare Robot using Attention-based Question-Answer Retrieval and Medical Entity Extraction Models

Journal
IEEE journal of biomedical and health informatics
Journal Volume
27
Journal Issue
12
Pages
6039–6050
Date Issued
2023-09-29
Author(s)
Chang, Yu-Hsuan
Guo, Yi-Ting
LI-CHEN FU  
Chiu, Ming-Jang
HAN-MO CHIU  
HUNG-JU LIN  
DOI
10.1109/JBHI.2023.3320939
URI
https://pubmed.ncbi.nlm.nih.gov/37773912/
https://scholars.lib.ntu.edu.tw/handle/123456789/636220
Abstract
In healthcare facilities, answering the questions from the patients and their companions about the health problems is regarded as an essential task. With the current shortage of medical personnel resources and an increase in the patient-to-clinician ratio, staff in the medical field have consequently devoted less time to answering questions for each patient. However, studies have shown that correct healthcare information can positively improve patients' knowledge, attitudes, and behaviors. Therefore, delivering correct healthcare knowledge through a question-answering system is crucial. In this paper, we develop an interactive healthcare question-answering system that uses attention-based models to answer healthcare-related questions. Attention-based transformer models are utilized to efficiently encode semantic meanings and extract the medical entities inside the user query individually. These two features are integrated through our designed fusion module to match against the pre-collected healthcare knowledge set, so that our system will finally give the most accurate response to the user in real-time. To improve the interactivity, we further introduce a recommendation module and an online web search module to provide potential questions and out-of-scope answers. Experimental results for question-answer retrieval show that the proposed method has the ability to retrieve the correct answer from the FAQ pairs in the healthcare domain. Thus, we believe that this application can bring more benefits to human beings.
Subjects
Data models | Deep Learning | Feature extraction | Healthcare Question-Answering System | Human-Robot Interaction | Medical Entity Extraction | Medical services | Neural networks | Robots | Search engines | Transformers
Type
journal article

臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

總館學科館員 (Main Library)
醫學圖書館學科館員 (Medical Library)
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開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

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