A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services

A cancer pathology report is a valuable medical document that provides information for clinical management of the patient and evaluation of health care. However, there are variations in the quality of reporting in free-text style formats, ranging from comprehensive to incomplete reporting. Moreover,...

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Autores principales:	Okechinyere J. Achilonu, Victor Olago, Elvira Singh, René M. J. C. Eijkemans, Gideon Nimako, Eustasius Musenge
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pathology reports breast colorectal prostate text mining machine learning Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/611d2a80aac04663b70ecec288748ed4
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spelling	oai:doaj.org-article:611d2a80aac04663b70ecec288748ed42021-11-25T17:58:29ZA Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services10.3390/info121104512078-2489https://doaj.org/article/611d2a80aac04663b70ecec288748ed42021-10-01T00:00:00Zhttps://www.mdpi.com/2078-2489/12/11/451https://doaj.org/toc/2078-2489A cancer pathology report is a valuable medical document that provides information for clinical management of the patient and evaluation of health care. However, there are variations in the quality of reporting in free-text style formats, ranging from comprehensive to incomplete reporting. Moreover, the increasing incidence of cancer has generated a high throughput of pathology reports. Hence, manual extraction and classification of information from these reports can be intrinsically complex and resource-intensive. This study aimed to (i) evaluate the quality of over 80,000 breast, colorectal, and prostate cancer free-text pathology reports and (ii) assess the effectiveness of random forest (RF) and variants of support vector machine (SVM) in the classification of reports into benign and malignant classes. The study approach comprises data preprocessing, visualisation, feature selections, text classification, and evaluation of performance metrics. The performance of the classifiers was evaluated across various feature sizes, which were jointly selected by four filter feature selection methods. The feature selection methods identified established clinical terms, which are synonymous with each of the three cancers. Uni-gram tokenisation using the classifiers showed that the predictive power of RF model was consistent across various feature sizes, with overall F-scores of 95.2%, 94.0%, and 95.3% for breast, colorectal, and prostate cancer classification, respectively. The radial SVM achieved better classification performance compared with its linear variant for most of the feature sizes. The classifiers also achieved high precision, recall, and accuracy. This study supports a nationally agreed standard in pathology reporting and the use of text mining for encoding, classifying, and production of high-quality information abstractions for cancer prognosis and research.Okechinyere J. AchilonuVictor OlagoElvira SinghRené M. J. C. EijkemansGideon NimakoEustasius MusengeMDPI AGarticlepathology reportsbreastcolorectalprostatetext miningmachine learningInformation technologyT58.5-58.64ENInformation, Vol 12, Iss 451, p 451 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pathology reports breast colorectal prostate text mining machine learning Information technology T58.5-58.64
spellingShingle	pathology reports breast colorectal prostate text mining machine learning Information technology T58.5-58.64 Okechinyere J. Achilonu Victor Olago Elvira Singh René M. J. C. Eijkemans Gideon Nimako Eustasius Musenge A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services
description	A cancer pathology report is a valuable medical document that provides information for clinical management of the patient and evaluation of health care. However, there are variations in the quality of reporting in free-text style formats, ranging from comprehensive to incomplete reporting. Moreover, the increasing incidence of cancer has generated a high throughput of pathology reports. Hence, manual extraction and classification of information from these reports can be intrinsically complex and resource-intensive. This study aimed to (i) evaluate the quality of over 80,000 breast, colorectal, and prostate cancer free-text pathology reports and (ii) assess the effectiveness of random forest (RF) and variants of support vector machine (SVM) in the classification of reports into benign and malignant classes. The study approach comprises data preprocessing, visualisation, feature selections, text classification, and evaluation of performance metrics. The performance of the classifiers was evaluated across various feature sizes, which were jointly selected by four filter feature selection methods. The feature selection methods identified established clinical terms, which are synonymous with each of the three cancers. Uni-gram tokenisation using the classifiers showed that the predictive power of RF model was consistent across various feature sizes, with overall F-scores of 95.2%, 94.0%, and 95.3% for breast, colorectal, and prostate cancer classification, respectively. The radial SVM achieved better classification performance compared with its linear variant for most of the feature sizes. The classifiers also achieved high precision, recall, and accuracy. This study supports a nationally agreed standard in pathology reporting and the use of text mining for encoding, classifying, and production of high-quality information abstractions for cancer prognosis and research.
format	article
author	Okechinyere J. Achilonu Victor Olago Elvira Singh René M. J. C. Eijkemans Gideon Nimako Eustasius Musenge
author_facet	Okechinyere J. Achilonu Victor Olago Elvira Singh René M. J. C. Eijkemans Gideon Nimako Eustasius Musenge
author_sort	Okechinyere J. Achilonu
title	A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services
title_short	A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services
title_full	A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services
title_fullStr	A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services
title_full_unstemmed	A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services
title_sort	text mining approach in the classification of free-text cancer pathology reports from the south african national health laboratory services
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/611d2a80aac04663b70ecec288748ed4
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A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services

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