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HQADeepHelper: A Deep Learning System for Healthcare Question Answering

Published: 20 April 2020 Publication History

Abstract

It is challenging to generate high quality answers for healthcare queries in online platforms. Recent studies proposed deep models for healthcare question answering (HQA) tasks. However, these models have not been thoroughly compared, and they were only tested on self-created datasets. This paper demonstrates a novel system, denoted by HQADeepHelper, to facilitate the learning and practicing of deep models for HQA. We have implemented a wide spectrum of state-of-the-art deep models for HQA retrieval. Users can upload self-collected HQA datasets and knowledge graphs, and do simple configurations by selecting datasets, knowledge graphs, neural network models, and evaluation metrics. Based on user’s configuration specified, the system can automatically train and test the model, conduct extensive experimental evaluation of the models selected, and report comprehensive findings. The reports provide new insights about the strengths and weaknesses of deep models that can guide practitioners to select appropriate models for various scenarios. Moreover, users can download the datasets, knowledge graphs, experimental reports and source codes of neural network models for their own practice and evaluations further.

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Cited By

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  • (2021)Mass Media as a Mirror of the COVID-19 PandemicComputation10.3390/computation91201409:12(140)Online publication date: 13-Dec-2021
  • (2021)MVQASProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481971(4675-4679)Online publication date: 26-Oct-2021
  • (2021)How Context or Knowledge Can Benefit Healthcare Question AnsweringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3090253(1-1)Online publication date: 2021
  • Show More Cited By

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          cover image ACM Conferences
          WWW '20: Companion Proceedings of the Web Conference 2020
          April 2020
          854 pages
          ISBN:9781450370240
          DOI:10.1145/3366424
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          Publication History

          Published: 20 April 2020

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          Author Tags

          1. Healthcare question answering
          2. Neural network models

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          WWW '20: The Web Conference 2020
          April 20 - 24, 2020
          Taipei, Taiwan

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          Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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          View all
          • (2021)Mass Media as a Mirror of the COVID-19 PandemicComputation10.3390/computation91201409:12(140)Online publication date: 13-Dec-2021
          • (2021)MVQASProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481971(4675-4679)Online publication date: 26-Oct-2021
          • (2021)How Context or Knowledge Can Benefit Healthcare Question AnsweringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3090253(1-1)Online publication date: 2021
          • (2020)BuTTER: BidirecTional LSTM for Food Named-Entity Recognition2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378151(3550-3556)Online publication date: 10-Dec-2020

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