Text mining in radiology reports by statistical machine translation approach

Bodile, Anuradha K.; Kshirsagar, Manali

doi:10.1109/gcct.2015.7342797

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…To handle text and image feature analysis, a match report approach using a knowledge base was developed. The medical finding extractor with SVM classifiers have been utilized for text prediction and standard image feature extraction techniques have been used for image analysis (Bodile & Kshirsagar, 2015). Pre-processing of unstructured reports using basic NLP techniques such as lemmatization, stemming, stop word removal, etc., has significantly improved the performance of the overall disease classification tasks (Gong et al, 2008;Wang et al, 2020).…”

Section: Radiology Report Data and Image Processingmentioning

confidence: 99%

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Shetty¹,

Ananthanarayana²,

Mahale³

2022

AIP

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Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems. Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system. Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential. This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification. A brief outline of the existing medical multimodal data processing work is presented. The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications. The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies. The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks.

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Section: Radiology Report Data and Image Processingmentioning

confidence: 99%

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Shetty¹,

Ananthanarayana²,

Mahale³

2022

AIP

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show abstract

“…Anuradha Bodile, Manali Kshirsagar [2] describes the extraction of image and extraction of text methods are explained. Natural language processing method is used for extraction of text, stemming is used for getting the root word, term mapping is used to evaluate the particular word is repetitive.…”

Section: Related Workmentioning

confidence: 99%

“…Many radiology reports are produced in health organization daily [2]. Useful information are taking out from the clinical comments use to creates the reports for specific patients [7].…”

Section: Introductionmentioning

confidence: 99%

“…The physical therapy usually creates the use, to obtain the medical imaging like X-rays, CT-scan, MRI scan and images of ultrasound. It is suitable to extend the implementation of automatic clinical record system.Many radiology reports are produced in health organization daily [2]. Useful information are taking out from the clinical comments use to creates the reports for specific patients [7].…”

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confidence: 99%

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Identification of Disease by Using SVM Classifier

Khedikar¹,

Yadav²

2017

IJARCSSE

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In radiology field, the large number of radiology reports is generated every day but never use again. Radiology reports are in unstructured format and it is difficult to understand. Procedure of radiology reports is the part of medical field. This paper describes the classification of disease from the radiology reports. The proposed methodology includes image pre-processing, segmentation, feature extractions and classification. First take an input image then apply enhancement technique for pre-processing of input images. After completing the image preprocessing perform segmentation using k-means clustering algorithm, select the region of interest and extract the grayscale and texture features of segmented image. These features are used to train the support vector machine (SVM). Support vector machine is used for classification. Keyword: SVM, KNN, ANN, PNN I. INTRODUCTIONLarge number of radiology reports are daily generated hence classification of that reports in various ways by using methods like clustering. Clustering method is able to navigating and shortening documents have expecting lots politeness. Clustering have been examine for grouping the reports of radiology reports into main clusters, in sequence to discover the essential features of image. A knowledge set of creating the clusters of algorithmic rule for unsupervised and establishing the different medical domains use different lexical and semantic patterns.Medical imaging is that the process and techniques are applied to create the individual imaging of the structure for clinical analysis, classification and treatment. It is speedily increasing in medical field. The physical therapy usually creates the use, to obtain the medical imaging like X-rays, CT-scan, MRI scan and images of ultrasound. It is suitable to extend the implementation of automatic clinical record system.Many radiology reports are produced in health organization daily [2]. Useful information are taking out from the clinical comments use to creates the reports for specific patients [7].Automatic and inexpensive recognizable proof of medical images is very important. Software engineering and information technology are the most important part of the medical image processing, classification and clinical review. The proposed plan includes the image preprocessing, image segmentation and extraction of features and classification by suing support vector machine. Preprocessing of image utilized to obtain the improvement of better radiology reports. Save the brightness of variation resulting in the marginal blurring of native boundaries. Extraction of image features may be treated as quantitative measures of medical images essentially used for creation of radiology reports. Extraction of image features are carefully, it is certain that the set of image features will remove the very important information from the input files. SVM, rules of KNN, ANN, PNN i.e. Probabilistic Neural Network, Hidden markove model etc. furthermore many applications almost like identification of digit by han...

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“…This method referred to a specific technique that was statistical or epidemiological : this is where further study needs to be undertaken by the team [62], [63]. A statistical machine translation approach [64] and a Bayesian information extraction network for the Medline abstract [65] are used in the proposed text mining system to deal with this problem.…”

Section: A Biomedical Text Mining Reviewmentioning

confidence: 99%

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abed¹,

Yuan²,

Li³

2017

ijacsa

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Abstract-The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein identification, patient medical records, genome sequencing, medical imaging and a huge set of biomedical research data are being generated day to day. The increase of biomedical data consists of both structured and unstructured data. Subsequently, in a traditional database system (structured data), managing and extracting useful information from unstructured-biomedical data is a tedious job. Hence, mechanisms, tools, processes, and methods are necessary to apply on unstructured biomedical data (text) to get the useful business data. The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way. Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research. Text mining is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are diverse and span to multiple disciplines, ranging from biomedicine to legal, business intelligence and security. In this survey paper, the researcher identifies and discusses biomedical data (text) mining issues, and recommends a possible technique to cope with possible future growth.

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Text mining in radiology reports by statistical machine translation approach

Cited by 4 publications

References 9 publications

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Identification of Disease by Using SVM Classifier

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

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