Creating a rule based system for text mining of Norwegian breast cancer pathology reports

Weegar, Rebecka; Dalianis, Hercules

doi:10.18653/v1/w15-2609

Cited by 17 publications

(14 citation statements)

References 6 publications

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“…Yala et al [63] extracted breast cancer-related information from pathology notes for the following types: The gold classification labels were created in previous breast cancer studies. Weegar and Dalianis [64] created a pilot rule-based system for information extraction from breast cancer pathology notes in Norwegian -sentinel nodes, axillary nodes, tumor size, histological grade, ER, PR, Ki67, pT. The system was trained and tested on a very small dataset and conceived as a pilot study to generate fodder for a more sophisticated system whose architecture is presented by the authors.…”

Section: Ie and Normalizationmentioning

confidence: 99%

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Gonzalez-Hernandez¹,

Sarker²,

O’Connor³

et al. 2017

Yearb Med Inform

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SummaryBackground: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems.

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Section: Ie and Normalizationmentioning

confidence: 99%

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Gonzalez-Hernandez¹,

Sarker²,

O’Connor³

et al. 2017

Yearb Med Inform

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“…Only a few limited studies focused on non-English medical text, and many of them used similar rules-based and machine learning–based approaches. 4,7 Studies of extraction from medical Cyrillic languages are even rarer. 8-10 Although applications of deep learning for the specific task of medical text extraction are limited, they have recently been used for extracting International Classification of Diseases for Oncology, 3rd revision (ICD-O-3) codes with limited accuracy (F 1 score of 0.722).…”

Section: Introductionmentioning

confidence: 99%

Clinical Data Extraction and Normalization of Cyrillic Electronic Health Records Via Deep-Learning Natural Language Processing

Zhao¹

2019

JCO Clinical Cancer Informatics

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PURPOSEA substantial portion of medical data is unstructured. Extracting data from unstructured text presents a barrier to advancing clinical research and improving patient care. In addition, ongoing studies have been focused predominately on the English language, whereas inflected languages with non-Latin alphabets (such as Slavic languages with a Cyrillic alphabet) present numerous linguistic challenges. We developed deep-learning–based natural language processing algorithms for automatically extracting biomarker status of patients with breast cancer from three oncology centers in Bulgaria.METHODSWe used dual embeddings for English and Bulgarian languages, encoding both syntactic and polarity information for the words. The embeddings were subsequently aligned so that they were in the same vector space. The embeddings were used as input to convolutional or recurrent neural networks to derive the biomarker status of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2.RESULTSWe showed that we can resolve ambiguity in highly variable medical text containing both Latin and Cyrillic text. Final models incorporating both English and Bulgarian syntax and polarity embeddings achieved F1 scores of 0.90 or higher for all estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 biomarkers. The models were robust against human errors originally found in the training set. In addition, such models can be extended for analyzing text containing words not seen during training.CONCLUSIONBy using several techniques that incorporate dual-word embeddings encoding syntactic and polarity information in two languages followed by deep neural network architectures, we show that researchers can extract and normalize parameters within medical data. The principles described here can be used to analyze Cyrillic or Latin mixed medical text and extract other parameters.

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“…Information by WHO about the neglected tropical disease [13]. Text mining of cancer pathology report A rule based system is developed for comparing manual encoding pathology report to validate the performance of rule based system [2]. Information of WHO classification on leprosy [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

Mehta,

Dharamsey,

Domal

et al. 2020

IJITEE

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Leprosy is one of the major public health problems and listed among the neglected tropical diseases in India. It is also called Hansen's Diseases (HD), which is a long haul contamination by microorganisms Mycobacterium leprae or Mycobacterium lepromatosis.Untreated, leprosy can dynamic and changeless harm to the skin, nerves, appendages, and eyes. This paper intends to depict classification of leprosy cases from the main indication of side effects. Electronic Health Records (EHRs) of Leprosy Patients from verified sources have been generated. The clinical notes included in EHRs have been processed through Natural Language Processing Tools. In order to predict type of leprosy, Rule based classification method has been proposed in this paper. Further our approach is compared with various Machine Learning (ML) algorithms like Support Vector Machine (SVM), Logistic regression (LR) and performance parameters are compared.

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Creating a rule based system for text mining of Norwegian breast cancer pathology reports

Cited by 17 publications

References 6 publications

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Clinical Data Extraction and Normalization of Cyrillic Electronic Health Records Via Deep-Learning Natural Language Processing

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

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