Bag-of-Words Technique in Natural Language Processing: A Primer for                     Radiologists

Juluru, Krishna; Shih, Hao-Hsin; Murthy, Krishna Nand Keshava; Elnajjar, Pierre

doi:10.1148/rg.2021210025

Cited by 30 publications

(12 citation statements)

References 17 publications

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“…The clustering methods used in the underlying work, which are based on BoW representations [ 15 ], are listed here: k-means K-means clustering, using scikit-learn’s [ 16 ] python implementation. LDA LDA!…”

Section: Methodsmentioning

confidence: 99%

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Natural Language Processing in Diagnostic Texts from Nephropathology

et al. 2022

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Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination.

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Section: Methodsmentioning

confidence: 99%

“…The clustering methods used in the underlying work, which are based on BoW representations [15], are listed here:…”

Section: Clustering With Bag-of-words Approachesmentioning

confidence: 99%

Natural Language Processing in Diagnostic Texts from Nephropathology

et al. 2022

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“…Perhaps the most straightforward NLP approach views each text as a ‘bag of words’, upon which a range of techniques may be applied, ignoring word ordering, sentence construction or grammar (Juluru et al, 2021). Many words, however, convey different meanings according to their surrounding terms, rendering simple word counts error-prone in some situations.…”

Section: Nlp: Methods and Techniquesmentioning

confidence: 99%

Natural language processing to identify case factors in child protection court proceedings

Coulthard

Taylor

2022

Methodological Innovations

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Social work case files hold rich detail about the lives and needs of vulnerable groups. Traditional case-reading studies to gain generalisable knowledge are resource-intensive, however, and sample sizes thereby limited. The advent of ‘big data’ technology, and vast repositories of centrally stored electronic records offer social work researchers novel alternatives, including data linkage and predictive risk modelling using administrative data. Free-text documents, however – including assessments, reports, and case chronologies – remain a largely untapped resource. This paper describes how 5000 social work court statements held by the Child and Family Court Advisory Support Service in England (Cafcass) were analysed using natural language processing (NLP) based on simple rules and mathematical principles. Thirteen factors relating to harm and risk to children involved in care proceedings in England were identified by automated computer techniques, and almost 90% agreement with professional readers achieved when the factors were clear-cut. The study represents an innovative approach for social work research on complex social problems. In conclusion, the paper discusses learning points; practical implications; future research avenues; and the technical and ethical challenges of NLP.

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“…Subsequently, they compared the performance of the classifier with the assessment by a radiation oncologist in unambiguous cases. The radiomic classifier analyzing bag-of-words-based features 120 had an AUC of 0.937, and the radiation oncologist had an AUC of 0.777.…”

Section: Radiomics To Predict Treatment-related Side Effectsmentioning

confidence: 99%

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

Viswanathan

Gupta

Madabhushi

2022

American Society of Clinical Oncology Educational Book

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Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment–related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment–related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges—including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias—must be addressed for seamless clinical translation of these tools.

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Bag-of-Words Technique in Natural Language Processing: A Primer for Radiologists

Cited by 30 publications

References 17 publications

Natural Language Processing in Diagnostic Texts from Nephropathology

Natural Language Processing in Diagnostic Texts from Nephropathology

Natural language processing to identify case factors in child protection court proceedings

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

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