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Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 4 (2022)
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Abstract:
Since the beginning of the COVID-19 pandemic almost two years ago, there have been more than 700,000 scientific papers published on the subject. An individual researcher cannot possibly get acquainted with such a huge text corpus and, therefore, some help from artificial intelligence (AI) is highly needed. We propose the AI-based tool to help researchers navigate the medical papers collections in a meaningful way and extract some knowledge from scientific COVID-19 papers. The main idea of our approach is to get as much semi-structured information from text corpus as possible, using named entity recognition (NER) with a model called PubMedBERT and Text Analytics for Health service, then store the data into NoSQL database for further fast processing and insights generation. Additionally, the contexts in which the entities were used (neutral or negative) are determined. Application of NLP and text-based emotion detection (TBED) methods to COVID-19 text corpus allows us to gain insights on important issues of diagnosis and treatment (such as changes in medical treatment over time, joint treatment strategies using several medications, and the connection between signs and symptoms of coronavirus, etc.).
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Keyword:
BERT; COVID-19; knowledge extraction; knowledge graphs; NER; NLP; text-based emotion detection (TBED); transfer learning
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URL: https://doi.org/10.3390/bdcc6010004
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| 8 |
Towards a theoretical understanding of word and relation representation
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| 9 |
Representation of Explanations of Possibilistic Inference Decisions
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In: Symbolic and Quantitative Approaches to Reasoning with Uncertainty ; ECSQARU 2021: European Conference on Symbolic and Quantitative Approaches with Uncertainty ; https://hal-cea.archives-ouvertes.fr/cea-03406884 ; ECSQARU 2021: European Conference on Symbolic and Quantitative Approaches with Uncertainty, Sep 2021, Prague, Czech Republic. pp.513-527, ⟨10.1007/978-3-030-86772-0_37⟩ (2021)
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| 10 |
Injecting Inductive Biases into Distributed Representations of Text ...
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| 16 |
RefWUG: Diachronic Reference Word Usage Graphs for German ...
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| 18 |
Αναγνώριση νοηματικής γλώσσας με τεχνικές βαθιάς μηχανικής μάθησης ... : Deep learning based sign language recognition ...
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RefWUG: Diachronic Reference Word Usage Graphs for German ...
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