ArticleProcedia computer science2023
Natural Language Processing for Covid-19 Consulting System.
Article in Procedia computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed, 12 citations in OpenAlex.
- Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.Journal of healthcare informatics research · 2024Article
- A review of reinforcement learning for natural language processing and applications in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024Review
- Assessing the research landscape and clinical utility of large language models: a scoping review.BMC medical informatics and decision making · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world was taken aback when the Covid-19 pandemic hit in 2019. Ever since precautions have been taken to prevent the spreading or mutating of the virus, but the virus still keeps spreading and mutating. Scientists predict that the virus is going to stay for a long time but with reduced effectiveness. Recognizing the symptoms of the virus is essential in order to provide proper treatment for the virus. Visiting hospitals for consultation becomes quite difficult when people are supposed to maintain social distancing. Recently neural network generative models have shown impressive abilities in developing chatbots. However, using these neural network generative models that lack the required Covid specific knowledge to develop a Covid consulting system makes them difficult to be scaled. In order to bridge the gap between patients and a limited number of doctors we have proposed a Covid consulting agent by integrating the medical knowledge of Covid-19 with the neural network generative models. This system will automatically scan patient's dialogues seeking for a consultation to recognize the symptoms for Covid-19. The transformer and pretrained systems of BERT-GPT and GPT were fine-tuned CovidDialog-English dataset to generate responses for Covid-19 which were doctor-like and clinically meaningful to further solve the problem of the surging demand for medical consultations compared to the limited number of medical professionals. The results are evaluated and compared using multiple evaluation metrics which are NIST-
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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.