Constructing a knowledge-based heterogeneous information graph for medical health status classification

Pham, Thuan; Tao, Xiaohui; Zhang, Ji; Yong, Jianming

doi:10.1007/s13755-020-0100-6

Cited by 21 publications

(9 citation statements)

References 40 publications

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“…Based on the analysis of the features of the construction waste and surrounding ground objects in Section 3.1, we established a one-to-one knowledge classification rule [42] and divided ground objects into two categories: Construction waste and non-construction waste. The classification model of the knowledge rules also has two parts.…”

Section: Classification Of Knowledge Rulesmentioning

confidence: 99%

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

et al. 2021

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With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation.

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Section: Classification Of Knowledge Rulesmentioning

confidence: 99%

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

et al. 2021

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“…Pham et al [2] have suggested the construction of knowledge-based heterogeneous information graphs to be used for classifications of medical health status. He et al [3] created synthetic triples using conceptualization, formulating the challenge as a triple classification that was addressed employing a discriminatory model, transferring knowledge from previously prepared language models.…”

Section: Knowledge Graph Construction/inferencementioning

confidence: 99%

From Tabulated Data to Knowledge Graph: A Novel Way of Improving the Performance of the Classification Models in the Healthcare Data

Zaki

Habuza

2021

Preprint

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In sectors like healthcare, having classification models that are both reliable and accurate is vital. Regrettably, contemporary classification techniques employing machine learning disregard the correlations between instances within data. This research, to rectify this, introduces a basic but effective technique for converting tabulated data into data graphs, incorporating structural correlations. Graphs have a unique capacity to capture structural correlations between data, allowing us to gain a deeper insight in comparison to carrying out isolated data analysis. The suggested technique underwent testing once the integration of graph data structure-related elements had been carried out and returned superior results to testing solely employing original features. The suggested technique achieved validity by returning significantly improved levels of accuracy.

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“…Nonetheless, the data source was speci c to claims data, which generally contain simple datasets compared to EMR data, thus limiting the data inclusion for reliable gestimation in predictions. Additionally, several studies have tackled integration of heterogeneous structured graphs combined with the attribute aspects [28][29][30][31][32] . Yet, the methods depicted in these studies do not t well with the properties of the EMR in network integration.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous graph construction and HinSAGE learning from electronic medical records

Cho

Ahn

Gwon

et al. 2022

Preprint

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Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of patient’s prognosis using HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network which provides analytical insights using graph representation of a patient’s journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model successfully predicted as baseline models. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular diseases event outcomes on supervised link prediction learning.

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Constructing a knowledge-based heterogeneous information graph for medical health status classification

Cited by 21 publications

References 40 publications

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

From Tabulated Data to Knowledge Graph: A Novel Way of Improving the Performance of the Classification Models in the Healthcare Data

Heterogeneous graph construction and HinSAGE learning from electronic medical records

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