Exploiting the semantic graph for the representation and retrieval of medical documents

Zhao, Qing; Kang, Yangyang; Li, Jianqiang; Wang, Dan

doi:10.1016/j.compbiomed.2018.08.009

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…The results and discussion of proposed DPCA-MGAC technique and existing methods are described with various performance metrics such as precision, false positive rate and time complexity. With the help of these parametric analysis, the comparison between three methods namely DPCA-MGAC technique, GSRM and LKNN is performed [9], [10]. The comparison results of three methods are explained in following section.…”

Section: Resultsmentioning

confidence: 99%

“…A semi-supervised spectral clustering method was introduced for grouping the MEDLINE Documents [9]. However, error rate was not considered in that for improving their performance.…”

Section: Related Workmentioning

confidence: 99%

“…An experimental evaluation of the proposed DPCA-MGAC technique and existing methods namely Graphbased Semantic Ranking Model (GSRM) and LKNN (Lexical KNN) algorithms are implemented using MATLAB and PubMed database as input [9], [10].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…It is reduced at the preprocessing stage to improve classification accuracy. A Graph-based Semantic Ranking Model (GSRM) has been introduced for retrieving the medical documents through the semantic search based classification [9]. But the false positive rate during the classification was not minimized.…”

Section: Introductionmentioning

confidence: 99%

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Discriminant Pearson Correlative Feature Selection based Gentle Adaboost Classification for Medical Document Mining

2019

IJRTE

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This paper examines Discriminant Pearson Correlative Analysis Based Multivariate Gentle Adaboost Classification (DPCA-MGAC) and it is used to improve the performance of medical document mining with minimum time complexity. A large number of documents are collected from PubMed databases through the semantic-based search. Processes such as removing stop words, stemming, features identification, selection of features i.e., relevant keywords for document classification are carried out. The significant feature selection is carried out using DPCA, and with the selected features the documents are categorized into different classes using MGAC. This classification process combines the results of all weak learners and makes a strong classification in order to improve the precision of medical data mining and minimizes the false positive rate. Experimental evaluation has been performed using PubMed database.

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Section: Resultsmentioning

confidence: 99%

“…A semi-supervised spectral clustering method was introduced for grouping the MEDLINE Documents [9]. However, error rate was not considered in that for improving their performance.…”

Section: Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminant Pearson Correlative Feature Selection based Gentle Adaboost Classification for Medical Document Mining

2019

IJRTE

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“…There is a need to rank the document based on the popularity because there are a number of documents available online for the single query that makes it difficult for the user to retrieve relevant information [3]. The web provides enormous information easily than searching for relevant information in local libraries [4].…”

Section: Introductionmentioning

confidence: 99%

AEAO: Auto Encoder with Adam Optimizer Method for Efficient Document Indexing of Big Data

Bhargavi*¹,

Murthy²,

Rao³

2019

IJRTE

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In the big data era, the document classification became an active research area due to the explosive nature in the volumes of data. Document Indexing is one of the important tasks under text classification. The objective of this research is to increase the performance of the document indexing by proposing Adam optimizer in the auto-encoder. Due to the larger dimension and multi-class classification problem, the accuracy of document indexing is reduced. In this paper, an enhanced auto encoder is used based on the objective function of the Adam optimization (AEAO), which improves the learning rate and accuracy of indexing. The documents from the 20-newsgroup data set are converted into vector representation, and then the cosine similarity and Pearson correlation have been measured from the vector. The word to vector representation has words in the vector form and the frequency of words in the document increases their value. The Adam optimization technique selects the features by using similarity values and improves the learning rate. The auto encoder classifier classifies the document based on the objective function of the Adam optimizer. The experiment is conducted using python and the result infers that the classification performance of AEAO is better than that of Similarity-based classification framework for Multiple-Instance Learning and Self-Adaptive LSH encoding for multi-instance Learning techniques in terms of parameters like precision, recall and fscore

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A Medical Decision Support System Using Text Mining to Compare Electronic Medical Records

Lee

Kang

et al. 2019

HCI in Business, Government and Organizations. Information Systems and Analytics

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Exploiting the semantic graph for the representation and retrieval of medical documents

Cited by 12 publications

References 18 publications

Discriminant Pearson Correlative Feature Selection based Gentle Adaboost Classification for Medical Document Mining

Discriminant Pearson Correlative Feature Selection based Gentle Adaboost Classification for Medical Document Mining

AEAO: Auto Encoder with Adam Optimizer Method for Efficient Document Indexing of Big Data

A Medical Decision Support System Using Text Mining to Compare Electronic Medical Records

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