Efficient Encoding of Pathology Reports Using Natural Language Processing

Weegar, Rebecka; Nygård, Jan F.; Dalianis, Hercules

doi:10.26615/978-954-452-049-6_100

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…Today, this is a manual and time‐consuming process that involves reading the pathology report and then reporting the numbers and data found. This could easily be automated using NLP techniques, specifically machine learning based mining, since there are lots of older manually coded pathology reports (Spasic et al, 2014; Weegar et al, 2017; Weegar & Dalianis, 2015). Weegar and Dalianis (2015) used rule‐based algorithms since the pathology reports were well structured.…”

Section: Unstructured Data Analysismentioning

confidence: 99%

Challenges and opportunities beyond structured data in analysis of electronic health records

Tayefi¹,

Ngo²,

Chomutare³

et al. 2021

WIREs Computational Stats

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140

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Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time‐consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well‐designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text. This article is categorized under: Applications of Computational Statistics > Health and Medical Data/Informatics

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Section: Unstructured Data Analysismentioning

confidence: 99%

Challenges and opportunities beyond structured data in analysis of electronic health records

Tayefi¹,

Ngo²,

Chomutare³

et al. 2021

WIREs Computational Stats

Self Cite

140

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“…The third study is by Weegar et al (2017) that used a much larger subset of pathology reports for prostate cancer than used by both Singh et al (2015) and Dahl et al (2016). Weegar et al (2017) built a rule-based system which extracted structured information from over 554 pathology reports for prostate cancer written in Norwegian. The authors divided the 554 pathology reports in 388 documents for the development set and 176 documents for the test set.…”

Section: The Case Of the Cancer Registry Of Norwaymentioning

confidence: 99%

“…The text contains descriptions of 9 biopsies, four from the left side and five from the right side. Figure published inWeegar et al (2017)…”

mentioning

confidence: 99%

Clinical Text Mining

Dalianis

2018

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“…With the continued growth in the number of cancer patients, and the increase in treatment complexity, cancer registries face a significant challenge in manually reviewing the large quantity of reports [1], [2]. In this situation, Natural Language Processing (NLP) systems can offer a unique opportunity to automatically encode the unstructured reports into structured data.…”

Section: Introductionmentioning

confidence: 99%

“…NLP approaches for information extraction within the biomedical research areas range from rule-based systems [4], to domain-specific systems using feature-based classification [2], to the recent deep networks for end-to-end feature extraction and classification [1]. NLP has had varied degree of success with free-text pathology reports [5].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Pathology Reports using TF-IDF Features

Kalra,

Li,

Tizhoosh

2019

Preprint

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Pathology report is arguably one of the most important documents in medicine containing interpretive information about the visual findings from the patient's biopsy sample. Each pathology report has a retention period of up to 20 years after the treatment of a patient. Cancer registries process and encode high volumes of free-text pathology reports for surveillance of cancer and tumor diseases all across the world. In spite of their extremely valuable information they hold, pathology reports are not used in any systematic way to facilitate computational pathology. Therefore, in this study we investigate automated machinelearning techniques to identify/predict the primary diagnosis (based on ICD-O code) from pathology reports. We performed experiments by extracting the TF-IDF features from the reports and classifying them using three different methods-SVM, XG-Boost, and Logistic Regression. We constructed a new dataset with 1,949 pathology reports arranged into 37 ICD-O categories, collected from four different primary sites, namely lung, kidney, thymus, and testis. The reports were manually transcribed into text format after collecting them as PDF files from NCI Genomic Data Commons public dataset. We subsequently pre-processed the reports by removing irrelevant textual artefacts produced by OCR software. The highest classification accuracy we achieved was 92% using XGBoost classifier on TF-IDF feature vectors, the linear SVM scored 87% accuracy. Furthermore, the study shows that TF-IDF vectors are suitable for highlighting the important keywords within a report which can be helpful for the cancer research and diagnostic workflow. The results are encouraging in demonstrating the potential of machine learning methods for classification and encoding of pathology reports.

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Efficient Encoding of Pathology Reports Using Natural Language Processing

Cited by 10 publications

References 6 publications

Challenges and opportunities beyond structured data in analysis of electronic health records

Challenges and opportunities beyond structured data in analysis of electronic health records

Clinical Text Mining

Automatic Classification of Pathology Reports using TF-IDF Features

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