A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years

• Published in: Diagnostics (Basel) Vol. 13; no. 3; p. 537
• Main Authors: Li, Chengtai, Weng, Ying, Zhang, Yiming, Wang, Boding
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2023; MDPI
• Subjects: Algorithms; Artificial intelligence; Breast cancer; Computational linguistics; Deep learning; Diagnosis; Electronic health records; Health aspects; Language processing; Machine learning; Natural language interfaces; Natural language processing; Neural networks; Researchers; Systematic Review; Trends; China; breast cancer; natural language processing; machine learning; artificial intelligence
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13030537

Artificial intelligence (AI) has been steadily developing in the medical field in the past few years, and AI-based applications have advanced cancer diagnosis. Breast cancer has a massive amount of data in oncology. There has been a high level of research enthusiasm to apply AI techniques to assist in breast cancer diagnosis and improve doctors' efficiency. However, the wise utilization of tedious breast cancer-related medical care is still challenging. Over the past few years, AI-based NLP applications have been increasingly proposed in breast cancer. In this systematic review, we conduct the review using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and investigate the recent five years of literature in natural language processing (NLP)-based AI applications. This systematic review aims to uncover the recent trends in this area, close the research gap, and help doctors better understand the NLP application pipeline. We first conduct an initial literature search of 202 publications from Scopus, Web of Science, PubMed, Google Scholar, and the Association for Computational Linguistics (ACL) Anthology. Then, we screen the literature based on inclusion and exclusion criteria. Next, we categorize and analyze the advantages and disadvantages of the different machine learning models. We also discuss the current challenges, such as the lack of a public dataset. Furthermore, we suggest some promising future directions, including semi-supervised learning, active learning, and transfer learning.