ArticleScientific reports2020
Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Natural Language Processing Methods Automate Molecular Marker Extraction From Glioma Pathology Reports.Neurosurgery · 2026Article
- Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.Journal of Korean medical science · 2026Article
- Transforming unstructured breast cancer pathology reports into the Observational Medical Outcomes Partnership Common Data Model.BMC medical informatics and decision making · 2026Article
- Development of a Natural Language Processing Model for Extracting Kidney Biopsy Pathology Diagnoses.Kidney medicine · 2025Article
- Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical Pathology Reports.medRxiv : the preprint server for health sciences · 2025Article
- NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.Medical & biological engineering & computing · 2025Article
- Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.Scientific reports · 2025Article
- Incremental learning algorithm for dynamic evolution of domain specific vocabulary with its stability and plasticity analysis.Scientific reports · 2025Article
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- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Evaluation and utilisation of privacy enhancing technologies-A data spaces perspective.Data in brief · 2024Article
- Correcting spelling mistakes in Persian texts with rules and deep learning methods.Scientific reports · 2023Article
- Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.Bioengineering (Basel, Switzerland) · 2023Article
- Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning.Nature biomedical engineering · 2022Article
- A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora.BMC medical informatics and decision making · 2022Article
- Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms.International journal of environmental research and public health · 2022Article
- Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary.Applied clinical informatics · 2022Article
- Comparison of Machine-Learning Algorithms for the Prediction of Current Procedural Terminology (CPT) Codes from Pathology Reports.Journal of pathology informatics · 2022Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Pathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword extraction for pathology reports is necessary to summarize the informative text and reduce intensive time consumption. In this study, we employed a deep learning model for the natural language process to extract keywords from pathology reports and presented the supervised keyword extraction algorithm. We considered three types of pathological keywords, namely specimen, procedure, and pathology types. We compared the performance of the present algorithm with the conventional keyword extraction methods on the 3115 pathology reports that were manually labeled by professional pathologists. Additionally, we applied the present algorithm to 36,014 unlabeled pathology reports and analysed the extracted keywords with biomedical vocabulary sets. The results demonstrated the suitability of our model for practical application in extracting important data from pathology reports.
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