Open Knowledge Accessing Method in IoT-Based Hospital Information System for Medical Record Enrichment

Xie, Cheng; Yang, Po; Ye, Yun

doi:10.1109/access.2018.2810837

Cited by 48 publications

(16 citation statements)

References 27 publications

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“…The research on the Health area, there is a paradigm shift from traditional and IoT-based medical field care, where doctors now have to pay more attention to the patient's raw medical records, then directly in making medical advice, conclusions or diagnoses from their experience using the hospital information system (HIS). The IoT-based HIS is distributed by scattered devices such as tablet computers, personal digital assistants that are used as automatic analyzers, or other massive and informative medical devices (Xie et al., 2018) and the possibility of a diagnosis can be made by a doctor elsewhere when the doctor has time for special patients, so the diagnosis can be more precise.…”

Section: Resultsmentioning

confidence: 99%

Knowledge growth and development: internet of things (IoT) research, 2006–2018

Dachyar

Zagloel

Saragih

2019

Heliyon

100

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The term "Internet of Things" first appeared in publication paper since 2006, describing the paradigm of evolution concept that brought about by the presence of internet technology (Vermesan and Friess, 2015) which is very important in contemporary circumstances. This study conducted an in-depth analysis of the research material written on 26420 papers which focused on the published Internet of Things (IoT) research, starting from the firstly year IoT keyword appeared in 2006 until 2018. The selected paper is a combination of various disciplines and publications which are all indexed by Scopus wherein the article discusses IoT. IoT articles are classified using key attributes in sequence: the methodology used, general knowledge and applied concepts, and various general exploration topics. By using the Scientometrics method, this method will group the overall terms that appear frequently from the Scopus paper database according to keywords, titles, and abstracts. The resulting data is then studied to understand and distinguish trends that occur in the time span along with the general characteristics of the paper, in the mathematics visual scheme. All various issues that are considered in the paper's methodology selection, their studied and services innovations, and continuing discoveries on the characteristics, concepts, and processes applied to IoT success. Although it only involves scopus indexed paper, this study found a remarkable increase in the number of articles on IoT in each category of the paper. This study also reveals the direction of the regular discipline of knowledge. The use of the Scientometrics method makes the analysis able to focus on the movement of characteristics and IoT themes to researcher's direction that has not found at this time, as a comprehensive guide to further research and industry strategy that is more directed on concepts that support the 4th industrial revolution.

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Section: Resultsmentioning

confidence: 99%

Knowledge growth and development: internet of things (IoT) research, 2006–2018

Dachyar

Zagloel

Saragih

2019

Heliyon

100

View full text Add to dashboard Cite

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“…It also provides a stable online interface for both research and commercial access. Table 1 summarizes the above methods in detail [16]. It is observed from Table 1 that Wikify!…”

Section: Entity Extractionmentioning

confidence: 99%

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

et al. 2019

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The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.

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“…Numerous KGs, have been created and successfully applied to many real-world applications, from semantic parsing [26], [27] to information extraction [28], [29], such as Freebase [30], DBpedia [31], YAGO [32], and NELL [33]. There are some specific applications of knowledge graphs, such as KGs in hospital data [34], industrial manufacturing forms [35], social network data [36], and user behavior data [37]. There are also music cognition [38].…”

Section: Knowledge Graphmentioning

confidence: 99%

Knowledge Graph-Based Image Classification Refinement

Zhang

Cui

et al. 2019

IEEE Access

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Biologically inspired ideas are important in image processing. Not only does more than 80% of the information received by humans comes from the visual system, but the human visual system also gives its fast, accurate, and efficient image processing capability. In the current image classification task, convolutional neural networks (CNNs) focus on processing pixels and often ignore the semantic relationships and human brain mechanisms. With the development of image analysis and processing techniques, the information in the image is becoming increasingly complicated. Humans can learn about the characteristics of objects and the relationships that occur between them to classify the images. It is a significant characteristic that sets humans apart from the modern learning-based computer vision algorithms. How to make full use of the semantic relationships in categories and how to apply the knowledge of biological vision to image classification are our main concerns. In this view, we propose the concept of the image knowledge graph (IKG) to incorporate the semantic association and the scene association to fully consider the relations between objects (external and internal). We take full advantage of the reasoning model of the knowledge graph that is closer to the biological visual information-processing model. We conduct extensive experiments on large-scale image datasets (ImageNet), demonstrating the effectiveness of our approach. Furthermore, our method participates in ILSVRC 2017 challenges and obtains the new state-of-the-art results on the ImageNet (82.43%).

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Open Knowledge Accessing Method in IoT-Based Hospital Information System for Medical Record Enrichment

Cited by 48 publications

References 27 publications

Knowledge growth and development: internet of things (IoT) research, 2006–2018

Knowledge growth and development: internet of things (IoT) research, 2006–2018

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

Knowledge Graph-Based Image Classification Refinement

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