ArticleJournal of pathology informatics2012
The feasibility of using natural language processing to extract clinical information from breast pathology reports.
Article in Journal of pathology informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.JCO clinical cancer informatics · 2019Pooled it
- Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.JCO clinical cancer informatics · 2019Pooled it
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
- Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.Journal of Korean medical science · 2026Article
- Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models.European heart journal. Digital health · 2025Article
- Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning.World journal of otorhinolaryngology - head and neck surgery · 2025Article
- MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers.BMC medical informatics and decision making · 2025Article
- ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.JCO clinical cancer informatics · 2025Article
- Article
- Automated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Models.medRxiv : the preprint server for health sciences · 2024Article
- BraNet: a mobil application for breast image classification based on deep learning algorithms.Medical & biological engineering & computing · 2024Article
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 2024Review
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.Journal of medical Internet research · 2022Article
- An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from pathology reports.Journal of pathology informatics · 2022Article
- Empowering digital pathology applications through explainable knowledge extraction tools.Journal of pathology informatics · 2022Article
- Searching Full-Text Anatomic Pathology Reports Using Business Intelligence Software.Journal of pathology informatics · 2022Article
- Automatic Classification of Cancer Pathology Reports: A Systematic Review.Journal of pathology informatics · 2022Article
- Comparison of Machine-Learning Algorithms for the Prediction of Current Procedural Terminology (CPT) Codes from Pathology Reports.Journal of pathology informatics · 2022Article
- Electronic case report forms generation from pathology reports by ARGO, automatic record generator for onco-hematology.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe opportunity to integrate clinical decision support systems into clinical practice is limited due to the lack of structured, machine readable data in the current format of the electronic health record. Natural language processing has been designed to convert free text into machine readable data. The aim of the current study was to ascertain the feasibility of using natural language processing to extract clinical information from >76,000 breast pathology reports. APPROACH AND PROCEDURE: Breast pathology reports from three institutions were analyzed using natural language processing software (Clearforest, Waltham, MA) to extract information on a variety of pathologic diagnoses of interest. Data tables were created from the extracted information according to date of surgery, side of surgery, and medical record number. The variety of ways in which each diagnosis could be represented was recorded, as a means of demonstrating the complexity of machine interpretation of free text.
resultsThere was widespread variation in how pathologists reported common pathologic diagnoses. We report, for example, 124 ways of saying invasive ductal carcinoma and 95 ways of saying invasive lobular carcinoma. There were >4000 ways of saying invasive ductal carcinoma was not present. Natural language processor sensitivity and specificity were 99.1% and 96.5% when compared to expert human coders.
conclusionWe have demonstrated how a large body of free text medical information such as seen in breast pathology reports, can be converted to a machine readable format using natural language processing, and described the inherent complexities of the task.
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