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

TL;DR

This paper presents elsarticle.cls, a reworked LaTeX class designed to format manuscripts for Elsevier journals while minimizing package conflicts. Built on the standard article class, it preserves the TeX kernel signature and integrates key packages (natbib, geometry, graphicx, hyperref, endfloat) to support consistent front matter, figures, and references, including model-specific layouts ($1+$, $3+$, $5+$). It differentiates from the older elsart.cls by avoiding kernel redefinitions, offering default preprint formatting, and providing convenient theorem environments and authoring options compatible with various journal models. The article documents installation workflows via Elsevier author resources and CTAN, including building the class from source (elsarticle.dtx and elsarticle.ins) and updating the TeX filename databank. Overall, elsarticle.cls aims to streamline submission workflows, improve robustness in handling complex documents, and reduce formatting inconsistencies across Elsevier journals.

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.