ArticleQuantitative imaging in medicine and surgery2023
Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Advancing rheumatology with natural language processing: insights and prospects from a systematic review.Rheumatology advances in practice · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: When users inquire about knowledge in a certain field using the internet, the intelligent question-answering system based on frequently asked questions (FAQs) provides numerous concise and accurate answers that have been manually verified. However, there are few specific question-answering systems for chronic diseases, such as rheumatoid arthritis, and the related technology to construct a question-answering system about chronic diseases is not sufficiently mature. Methods: Our research embedded the classification information of the question into the sentence vector based on the bidirectional encoder representations from transformers (BERT) language model. First of all, we calculated the similarity using edit distance to recall the candidate set of similar questions. Then, we took advantage of the BERT pretraining model to map the sentence information to the corresponding embedding representation. Finally, each dimensional feature of the sentence was obtained by passing a sentence vector through the multihead attention layer and the fully connected feedforward layer. The features that were stitched and fused were used for the semantic similarity calculation. Results: Our improved model achieved a Top-1 precision of 0.551, Top-3 precision of 0.767, and Top-5 precision of 0.813 on 176 testing question sentences. In the analysis of the actual application effect of the model, we found that our model performed well in understanding the actual intention of users. Conclusions: Our deep learning model takes into account the background and classifications of questions and combines the efficiency of deep learning technology and the comprehensibility of semantics. The model enables the deep meaning of the user's question to be better understood by the intelligent question answering system, and answers that are more relevant to the original query are provided.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.