Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records
Open accessAbstract
Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.
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Natural language processing increases productivity in clinical oncology by converting unstructured text into analyzable data and standardizing documentation.
“Natural language processing (NLP) increases productivity in clinical oncology by converting unstructured text into analyzable data and standardizing documentation [41,42,43,44,45,46].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [45]
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References 87 · Cited by 170
Research works printed but not linked: 6
- [36]Bethard S, Savova G, Palmer M & Pustejovsky J SemEval-2017 Task 12: Clinical TempEval. in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) 565– 572 (Association for Computational Linguistics, 2017).
- [39]Dligach D, Miller T, Lin C, Bethard S & Savova G Neural Temporal Relation Extraction. in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers 746–751 (Association for Computational Linguistics, 2017).
- [41]Lin Chen, Miller Timothy, Dligach Dmitriy, Bethard Steven & Savova Guergana. A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction. in Clinical NLP Workshop (2019).
- [49]HPC-Based Hyperparameter Search of MT-CNN for Information Extraction from Cancer Pathology Reports. Available at: https://sc18.supercomputing.org/proceedings/workshops/workshop_pages/ws_cafcw107.html. (Accessed: 12th February 2019)
- [63]Bergquist SL Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data. 14 PMC6287925 30542673
- [69]Leeuwenberg A & Moens M-F Word-Level Loss Extensions for Neural Temporal Relation Classification. in Proceedings of the 27th International Conference on Computational Linguistics 3436– 3447 (2018).
- Authors
- Savova, G. K., Danciu, I., Alamudun, F., Miller, T., Lin, C., Bitterman, D. S., Tourassi, G., & Warner, J. L.
- Affiliations
- Boston Children's Hospital (US)
- 3Oak Ridge National Lab, Knoxville, Tennessee.
- Harvard University (US)
- Vanderbilt University Medical Center (US)
- Corresponding author
- Savova, Guergana K.
- ORCID
- Venue
- Cancer Research
- Published
- 2019-11-01
- Volume
- 79
- Issue
- 21
- Pages
- 5463-5470
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- PMC
- PMC7227798
- OpenAlex
- W2967056613
- Cited by
- 170
- References
- 87
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 36 · 2023: 41 · 2022: 26 · 2021: 14 · 2020: 13 · 2018: 2
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NCI (U24CA184407, U01CA231840)
- Oak Ridge National Laboratory (DE-AC05-00OR22725)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- natural language processing, clinical text, cancer phenotypes, information extraction, electronic health records, real-world data, multi-omics integration, clinical phenotyping
- MeSH terms
- Machine Learning, Humans, Medical Oncology, Natural Language Processing, Phenotype, Data Mining, Electronic Health Records
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