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This study aims to review the recent advances in community challenges for biomedical text mining in China.

We collected information of evaluation tasks released in community challenges of biomedical text mining, including task ...

Ontologies play a key role in representing and structuring domain knowledge. In the biomedical domain, the need for this type of representation is crucial for structuring, coding, and retrieving data. However, available ontologies do not encompass ...

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The primary objective of this review is to investigate the effectiveness of machine learning and deep learning methodologies in the context of extracting adverse drug events (ADEs) from clinical benchmark datasets. We conduct an in-...

The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, ...

• Proposed WE-iMKVec increases the performance of standard word embeddings in deep learning-based sentiment classification
• iMKVec generation using sentiment lexicons, ADR word lists developed by the medical experts, and UMLS is proposed

Text-Based Medical Image Retrieval (TBMIR) has been known to be successful in retrieving medical images with textual descriptions. Usually, these descriptions are very brief and cannot express the whole visual content of the image in ...

• Proposing a Rule-Based Bayesian Network method to extract the term dependency.
• ...

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Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent ...

Automatically extracting relationships from biomedical texts among multiple sorts of entities is an essential task in biomedical natural language processing with numerous applications, such as drug development or repurposing, precision medicine, ...

In this study, we propose a new approach for cross-lingual biomedical concept normalization, the process of mapping text in non-English documents to English concepts of a knowledge base. The resulting mappings, named as semantic annotations, ...

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To propose a new vector-based relatedness metric that derives word vectors from the intrinsic structure of biomedical ontologies, without consulting external resources such as large-scale biomedical corpora.

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically ...

Tools to explore scientific literature are essential for scientists, especially in biomedicine, where about a million new papers are published every year. Many such tools provide users the ability to search for specific entities (e.g. proteins, diseases) ...

The Animal and Plant Health Agency (APHA) conducts post-mortem examinations (PMEs) of farm animal species as part of routine scanning surveillance for new and re-emerging diseases that may pose a threat to animal and public health. This paper ...

StudyPortal was implemented as the first multilingual search platform for geographic visualization of clinical trials and scientific articles. The platform queries information from , PubMed, a geodatabase and geographic maps to ...

UMLS (Unified Medical Language System) is one of the most comprehensive terminological resources for the medical domain. Thus, the provision of instruments to assist in the cataloguing of medical reports with UMLS is of utmost relevance, especially when ...

Respiratory tract infections are most common disease which can affect any human during any part of their age. According to sources almost 60% of all antibiotic prescription is due to Respiratory tract infection. The concepts and their relations related ...

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• Built a large-scale disease organ network (DON) based on the UMLS Metathesaurus.

Discerning the modular nature of human diseases through computational approaches calls for diverse data. The finding sites of diseases, like other disease phenotypes, possess rich information in understanding disease genetics. Yet, ...

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• Medical semantic resources were developed to discover hidden knowledge in text.

In the medical image retrieval literature, there are two main approaches: content-based retrieval using the visual information contained in the image itself and context-based retrieval using the metadata and the labels associated with ...

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• Same parent concepts in different terminologies often do not have the same children.

In previous research, we have studied concepts that occur in pairs of medical terminologies and are known to be identical, because they have the same ID number in the Unified Medical Language System (UMLS). We observed that such ...

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• Measuring the relatedness of biological concepts in UMLS is necessary in many fields.

There have been many attempts to identify relationships among concepts corresponding to terms from biomedical information ontologies such as the Unified Medical Language System (UMLS). In particular, vector representation of such ...

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• Unsupervised concept extraction from clinical text.
• Uses semantic ...

Concept extraction is an important step in clinical natural language processing. Once extracted, the use of concepts can improve the accuracy and generalization of downstream systems. We present a new unsupervised system for the ...