MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

Karâa, Wahiba Ben Abdessalem; Ashour, Amira S.; Sassi, Dhekra Ben; Roy, Payel; Kausar, Noreen; Dey, Nilanjan

doi:10.1007/978-3-319-21212-8_12

Cited by 56 publications

(13 citation statements)

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“…This study uses the standard information retrieval metrics precision, recall, and f-measure [ 56 – 59 ] to evaluate the proposed model. Precision intends to evaluate the cluster based on the number of correctly retrieved instances out of the total number of retrieved queries.…”

Section: Resultsmentioning

confidence: 99%

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

et al. 2021

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Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

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Section: Resultsmentioning

confidence: 99%

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

et al. 2021

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“…This issue was addressed with a fuzzy similaritybased data cleansing approach; The authors applied vector space and topic modelling to extract the rich patient-specific information available in unstructured clinical data. This can be crucial in countries where structured EHR adoption is not widespread [31,32]. The authors worked with 223, 556 nursing notes of 357.8 words on average, predicting 19 ICD-9 code group labels, and achieved a maximum F1-score (weighted-) of 69.81 across all the tested models.…”

Section: Related Workmentioning

confidence: 99%

Extreme Multi-Label ICD Classification: Sensitivity to Hospital Service and Time

2020

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This work deals with clinical text mining for automatic classification of Electronic Health Records (EHRs) with respect to the International Classification of Diseases (ICD). ICD is the international standard for the identification of diseases and health conditions in EHRs and the foundation for reporting health statistics. Machine learning-based techniques have proven robust to infer classification models from EHRs. Since each EHR tends to involve multiple diseases, multi-label classification is required. The concern in this work is the versatility of the models inferred and their ability to generalise in two ways: as time goes ahead and across hospital services or health specialties. Indeed, in this work, we show the capabilities of a Bidirectional Recurrent Neural Network (RNN) with GRU units and ELMo embeddings on two corpora (a corpus comprising a set of EHRs within the Basque Health System, namely Osakidetza, and the well-known MIMIC-III corpus). To delve into and assess the versatility of the models, we focus on their resilience across hospital admissions taken over two different years and also across six distinct hospital services. In addition, we paid attention to the classification performance to estimate ICD codes of different granularity (e.g. with or without essential modifiers). Our best results are 39.55% and 47.28% F-Score for the Osakidetza and MIMIC-III datasets respectively, with the original main label-sets. Regarding the models evaluated per specialty, the most remarkable results are 57.00% and 72.74% F-Score, in the Cardiology and Nephrology medical services respectively. INDEX TERMS Extreme multi-label classification, Electronic Health Records, International Classification of Diseases, Classification across-time, Classification across hospital-services.

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“…Chowdhuri et al [5] presented a review for Rough set based Ad Hoc network. Karaa et al [6] proposed a novel approach for document clustering MEDLINE based on genetic algorithm. Kausar et al [7] discussed the data mining method applied in the extraction of clinical attribute and classification for diagnosis of cardiovascular patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Contemporary Layout’s Integration for Geospatial Image Mining

Shaikh¹,

Li²,

Khan³

2016

ijacsa

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Abstract-Image taxonomy and repossession plays a major role in dealing with large multimedia data on the Internet. Social networks, image sharing websites and mobile application require categorizing multimedia items for more efficient search and storage. Therefore, image classification and retrieval methods gained a great importance for researchers and companies. Image classification can be performed in a supervised and semisupervised manner and in order to categorize an unknown image, a statistical model created using relabeled samples is fed with the numerical representation of the visual features of images. Analysis of the keywords surrounding the images or the content of the images alone has not yet achieved results that would allow deriving precise location information to select representative images. Photos that are reliably tagged with labels of place names or areas only cover a small fraction of available images and also remain at a keyword level. State of the art of content based retrieval has been analyzed in earth perception image archives concentrating on complete frameworks indicating guarantee for the operational implementation. The methods are taken into consideration, concentrating specifically on the stages after extraction of primitive features. The solutions conceived for the issues such as synthesis and simplification of features, semantic labeling and indexing are reviewed. The approaches regarding query execution and specification are assessed where conclusions are drawn in the research of earth observation mining.

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MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

Cited by 56 publications

References 50 publications

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

Extreme Multi-Label ICD Classification: Sensitivity to Hospital Service and Time

Contemporary Layout’s Integration for Geospatial Image Mining

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