A dictionary-guided attention network for biomedical named entity recognition in Chinese electronic medical records

Zhu, Zhichao; Li, Jianqiang; Quan, Zhao; Akhtar, Faheem

doi:10.1016/j.eswa.2023.120709

Cited by 10 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A transformer [ 11 ] is a deep learning model widely used for sequence-to-sequence tasks, having garnered significant acclaim in the field of natural language processing, particularly for machine translation, and subsequently finding broad research applications in other domains, including image processing. In the realm of medical diagnosis, A transformer proves valuable for processing and modeling diverse modalities of medical data, encompassing clinical texts, medical images, and time series data [ 59 , 60 , 61 ]. Primarily, leveraging the self-attention mechanism, the transformer computes relevance scores between each position in the input sequence and other positions.…”

Section: Multi-modal and Ai Used In Disease Diagnosismentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

Self Cite

View full text Add to dashboard Cite

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

show abstract

Section: Multi-modal and Ai Used In Disease Diagnosismentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

Knowledge Graph for Solubility Big Data: Construction and Applications

Haiyang,

Ruomei,

Yan

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Dissolution refers to the process in which solvent molecules and solute molecules attract and combine with each other. The extensive solubility data generated from the dissolution of various compounds under different conditions, is distributed across structured or semi‐structured formats in various media, such as text, web pages, tables, images, and databases. These data exhibit multi‐source and unstructured features, aligning with the typical 5 V characteristics of big data. A solubility big data technology system has emerged under the fusion of solubility data and big data technologies. However, the acquisition, fusion, storage, representation, and utilization of solubility big data are encountering new challenges. Knowledge Graphs, known as extensive systems for representing and applying knowledge, can effectively describe entities, concepts, and relations across diverse domains. The construction of solubility big data knowledge graph holds substantial value in the retrieval, analysis, utilization, and visualization of solubility knowledge. Throwing out a brick to attract a jade, this paper focuses on the solubility big data knowledge graph and, firstly, summarizes the architecture of solubility knowledge graph construction. Secondly, the key technologies such as knowledge extraction, knowledge fusion, and knowledge reasoning of solubility big data are emphasized, along with summarizing the common machine learning methods in knowledge graph construction. Furthermore, this paper explores application scenarios, such as knowledge question answering and recommender systems for solubility big data. Finally, it presents a prospective view of the shortcomings, challenges, and future directions related to the construction of solubility big data knowledge graph. This article proposes the research direction of solubility big data knowledge graph, which can provide technical references for constructing a solubility knowledge graph. At the same time, it serves as a comprehensive medium for describing data, resources, and their applications across diverse fields such as chemistry, materials, biology, energy, medicine, and so on. It further aids in knowledge retrieval and mining, analysis and utilization, and visualization across various disciplines.

show abstract

ResiAdvNet: A named entity recognition model for potato diseases and pests based on progressive residual structures and adversarial training

Tang,

Wen,

et al. 2024

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

A dictionary-guided attention network for biomedical named entity recognition in Chinese electronic medical records

Cited by 10 publications

References 31 publications

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Knowledge Graph for Solubility Big Data: Construction and Applications

ResiAdvNet: A named entity recognition model for potato diseases and pests based on progressive residual structures and adversarial training

Contact Info

Product

Resources

About