# Natural Language Processing for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review

arXiv: https://arxiv.org/abs/2410.22180 · HTML: https://mlsanity.com/paper/2410.22180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 125 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Zero-shot information extraction from radiological reports using ChatGPT](https://mlsanity.com/paper/2309.01398/) (2023)
- [A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models](https://mlsanity.com/paper/2108.04949/) (2021)
- [SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies](https://mlsanity.com/paper/2212.03000/) (2022)
- [A frame semantic overview of NLP-based information extraction for cancer-related EHR notes](https://mlsanity.com/paper/1904.01655/) (2019)
- [Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy](https://mlsanity.com/paper/2303.13722/) (2023)
- [Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods](https://mlsanity.com/paper/2304.00115/) (2023)
- [Unsupervised extraction, labelling and clustering of segments from clinical notes](https://mlsanity.com/paper/2211.11799/) (2022)
- [A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records](https://mlsanity.com/paper/2303.08448/) (2023)
- [Lung Cancer Concept Annotation from Spanish Clinical Narratives](https://mlsanity.com/paper/1809.06639/) (2018)

## Abstract (arXiv)

Objective: This review aims to analyze the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. This review addresses gaps in the existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. Methods: A comprehensive literature search was conducted using the Scopus database, identifying 94 relevant studies published between 2019 and 2024. Data extraction included study characteristics, cancer types, NLP methodologies, dataset information, performance metrics, challenges, and future directions. Studies were categorized based on cancer types and NLP applications. Results: The results showed a growing trend in NLP applications for cancer research, with breast, lung, and colorectal cancers being the most studied. Information extraction and text classification emerged as predominant NLP tasks. A shift from rule-based to advanced machine learning techniques, particularly transformer-based models, was observed. The Dataset sizes used in existing studies varied widely. Key challenges included the limited generalizability of proposed solutions and the need for improved integration into clinical workflows. Conclusion: NLP techniques show significant potential in analyzing EHRs and clinical notes for cancer research. However, future work should focus on improving model generalizability, enhancing robustness in handling complex clinical language, and expanding applications to understudied cancer types. Integration of NLP tools into clinical practice and addressing ethical considerations remain crucial for utilizing the full potential of NLP in enhancing cancer diagnosis, treatment, and patient outcomes.