Health care collaboration on the information highway

Greene, Marjorie

doi:10.1109/44.605948

Cited by 1 publication

(3 citation statements)

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“…First, some relationship categories are predefined by experts. When performing relationship extraction, it is necessary to design a method to identify which type the interaction between any two entities belongs to [ 3 ].…”

Section: Problem Definitionmentioning

confidence: 99%

“…Driven by various evaluation conferences and natural language processing applications, the goals of designated entity recognition tasks continue to expand. The Automatic Content Extraction (ACE) project expands the geographical and institutional name entities, adding facility and geopolitical entities [ 3 ]. The Automatic Content Extraction (ACE) software is dedicated to extracting data using audiovisual inputs.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, prior knowledge has a better role in promoting named entity recognition tasks, such as knowledge graphs. Literature [ 14 ] used the open-source named entity system DBpedia Spotlight to optimize the named entity recognition task [ 3 ]. Based on the original system, the Chinese-related knowledge expands the candidate set incrementally.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Multichannel CNN Model for Biomedical Entity Reorganization

Singh

Khan

et al. 2022

BioMed Research International

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Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%