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

TL;DR

This review highlights the growing use of NLP techniques in analyzing electronic health records and clinical notes for cancer research, emphasizing recent advances, challenges, and future directions in the field.

Contribution

It provides a comprehensive overview of NLP applications in cancer research, covering recent studies, methodologies, challenges, and future prospects beyond specific cancer types.

Findings

NLP applications are increasing in cancer research, especially for breast, lung, and colorectal cancers.

Transformer-based models are replacing rule-based and traditional machine learning methods.

Challenges include limited generalizability and integration into clinical workflows.

Abstract

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.