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Extracting cancer concepts from clinical notes using natural language processing: a systematic review
Maryam Gholipour1, Reza Khajouei2, Parastoo Amiri1
1Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran.
BMC Bioinformatics
|October 29, 2023
Summary
Natural Language Processing (NLP) effectively extracts cancer concepts from clinical notes, with rule-based algorithms showing high accuracy and sensitivity. Future research should leverage these NLP methods for broader disease concept extraction.
Area of Science:
- Medical Informatics
- Computational Linguistics
- Oncology
Background:
- Automated information extraction from clinical notes using Natural Language Processing (NLP) offers significant time and effort savings for cancer patient data analysis.
- This systematic review focuses on studies employing NLP techniques for the automatic identification of cancer-related concepts within clinical text.
Approach:
- A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Embase up to June 29, 2021.
- Keywords included "Cancer", "NLP", "Coding", and "Registries", with eligibility assessed by two independent reviewers.
Key Points:
- Rule-based algorithms were the predominant method for NLP software development in this domain.
- Accuracy and sensitivity were the most common metrics for evaluating algorithm performance.
- Studies predominantly focused on extracting concepts for breast and lung cancer, utilizing terminologies like SNOMED-CT and UMLS.
Conclusions:
- The application of NLP for cancer concept extraction has seen a notable increase.
- Rule-based algorithms are favored for their high accuracy and sensitivity in identifying cancer concepts.
- The study recommends the use of these established NLP algorithms for extracting concepts related to other diseases.
Keywords:
Information systemMachine learningNLPNatural language processingNeoplasmsSystematic reviewTerminologyMore Related Videos
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