Abstract
The impact of misinformation on the web is harmful to individuals and society. In healthcare, misinformation can even be life-threatening in extreme cases. Therefore, being able to assess the truthfulness and quality of health-related information is vital. In this paper, we introduce MedSeer, an open-source system that helps lay people to assess the quality of health-related statements and thus builds a bridge between individuals and the latest healthcare research. Scientific papers from reputable sources contain rich information that has usually undergone thorough peer review to guarantee the quality of its content. MedSeer uses these sources to harvest knowledge with reliable credibility and exploits the BERT question and answering model to highlight possible answers.
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Notes
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It should be noted that systems like MedSeer cannot replace a medical consultation.
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References
Ayers, S.L., Kronenfeld, J.J.: Chronic illness and health-seeking information on the internet. Health 3, 327–347 (2007)
Beltagy, I., Cohan, A., Lo, K.: SciBERT: pretrained contextualized embeddings for scientific text. CoRR (2019). http://arxiv.org/abs/1903.10676
Benigeri, M., Pluye, P.: Shortcomings of health information on the internet. Health Promot. Int. 4, 381–386 (2003)
Bhavnani, S.K.: Domain-specific search strategies for the effective retrieval of healthcare and shopping information. In: Extended abstracts of the 2002 Conference on Human Factors in Computing Systems (CHI 2002), pp. 610–611. ACM (2002). https://doi.org/10.1145/506443.506508
Bhavnani, S.K., Jacob, R.T., Nardine, J., Peck, F.A.: Exploring the distribution of online healthcare information, pp. 816–817. ACM (2003). https://doi.org/10.1145/765891.766009
Understanding the COVID-19 plasma treatment debate. https://healthjournalism.org/blog/2020/08/understanding-the-covid-19-plasma-treatment-debate/. Accessed 5 June 2022
GROBID. https://grobid.readthedocs.io/en/latest/Introduction/. Accessed 5 June 2022
Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. CoRR (2019). http://arxiv.org/abs/1901.08746
Mesquita, C.T., Oliveira, A., Seixas, F.L., Paes, A.: Infodemia, fake news and medicine: science and the quest for truth. Int. J. Cardiovasc. Sci. 33(3), 203–205 (2020). https://doi.org/10.36660/ijcs.20200073
People-Also-Ask API. https://pypi.org/project/people-also-ask/. Accessed 5 June 2022
pyserini 0.17.0. https://pypi.org/project/pyserini/. Accessed 5 June 2022
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR (2019). http://arxiv.org/abs/1910.01108
Sayed, D., Nour, M., Schuldt, H.: Seer-Dock: a general-purpose dockerized scholarly document collection and management framework. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), pp. 2485–2490. ACM (2021). https://doi.org/10.1145/3404835.3463251
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Sayed, D., Noureldin, M., Schuldt, H. (2022). MedSeer: A Medical Controversial Information Retrieval System Based on Credible Sources. In: Silvello, G., et al. Linking Theory and Practice of Digital Libraries. TPDL 2022. Lecture Notes in Computer Science, vol 13541. Springer, Cham. https://doi.org/10.1007/978-3-031-16802-4_57
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