Sentence-based classification of free-text breast cancer radiology reports

Maghsoodi, Aisan; Sevenster, Merlijn; Scholtes, Johannes C.; Nalbantov, Georgi

doi:10.1109/cbms.2012.6266374

Cited by 6 publications

(2 citation statements)

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“…Setting up a firm framework is mandatory to envision the narrative medical content. Maghsoodi et al (2012) considered an automatic sentence classification from radiology reports. Based on three features including modality, literality and recommendation, annotating sentences into seven classes is the core methodology of the proposed work and post tags are used for relevant processing.…”

Section: Radiology Report 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 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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“…The reports are not in proper format and usually unprocessed, making it difficult for radiology professionals to retrieve and use useful knowledge and information from the reports. Radiologist often face the burden of reviewing prior study reports before reading the patient's current study [10] and it's a very time consuming and difficult task. For making the clinical decision [2],it is advantageous to use the previous report of the same structure, of the same region, and of the same disease.…”

Section: Introductionmentioning

confidence: 99%

Text Mining in Radiology Reports by SVM Classifier

Bodile¹,

Kshirsagar²

2015

IJCA

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Medical text mining has gained increasing popularity in recent years. Now a days, large amount of medical text data are daily generated in health institutions, but never refer again as it is very time consuming task. In Radiology research area, most of the reports are in free text format and usually unprocessed, hence it is difficult to access the valuable information for medical professional unless proper text mining is not applied. There are some systems for radiology report information retrieval like MedLEE, NeuRadIR, CBIR but very few of them make use of text associated with image This paper proposes a text mining system to deals with this problem by using statistical machine translation approach. The radiology report is given to the system as input and system will return the similar report match with the entered report from the database.The SVM classifier is use in SMT approach to find the match report. Precision and Recall accuracy measures are used for evaluation purpose.

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