ArticleScientific reports2025
Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- "Do It by Myself" or Autonomy, Participation, and Assistive Devices and Technology Needs of Children and Youth With Disabilities: Text Mining Analysis of a National Survey in France.JMIR medical informatics · 2026Article
- Construction and Application of Traditional Chinese Medicine Knowledge Graph Based on Large Language Model.Interdisciplinary sciences, computational life sciences · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Rehabilitation-related diseases have long recovery times, making frequent hospital visits impractical for patients. There is a high demand for online rehabilitation advice, but valuable Q&A information in online health communities remains largely untapped, leading to wasted medical resources. This study developed a BERT-BiGRU-attention model to extract three types of entity relationships: disease symptoms, appropriate rehabilitation measures, and inappropriate rehabilitation measures. This model achieved optimal knowledge extraction results. We then used a clustering analysis model to group disease-related knowledge, helping to uncover useful information for rehabilitation patients, assist in medical diagnosis, and enhance health education.
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Registered trials
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