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Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.

Chengtai Li1, Yiming Zhang1, Ying Weng1

  • 1School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.

Diagnostics (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

Natural Language Processing (NLP) advances computer-aided diagnosis in oncology using electronic health records (EHR) and electronic medical records (EMR). This review analyzes NLP applications across seven cancer types, identifying limitations and future directions.

Keywords:
computer-aided diagnosiselectronic health recordselectronic medical recordsnatural language processingoncology

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Oncology
  • Natural Language Processing

Background:

  • Electronic Health Records (EHR) and Electronic Medical Records (EMR) are rapidly expanding data sources in oncology.
  • Extracting valuable clinical insights from unstructured EHR/EMR data is time-consuming for oncologists.
  • Natural Language Processing (NLP) offers potential solutions for automated data analysis and decision support.

Purpose of the Study:

  • To conduct a narrative review of recent advancements in NLP applications for computer-aided diagnosis in oncology.
  • To bridge the gap between AI expertise and clinical practice for improved NLP tool development.
  • To analyze NLP applications across diverse cancer types and identify current challenges and future research avenues.

Main Methods:

  • Systematic literature search across PubMed, Google Scholar, and ACL Anthology.
  • Screening of 295 identified articles, including deduplication and relevance assessment.
  • In-depth analysis and categorization of 23 selected studies based on cancer type.

Main Results:

  • NLP techniques, including rule-based, machine learning, and deep learning, are increasingly applied to EHR/EMR data for oncological diagnosis.
  • Studies covered seven major cancer types: breast, lung, liver, prostate, pancreatic, colorectal, and brain tumors.
  • Identified limitations in current NLP applications and proposed future research directions.

Conclusions:

  • NLP holds significant promise for enhancing computer-aided diagnosis and decision-making in oncology.
  • Further research is needed to address current limitations and foster collaboration between AI and clinical specialists.
  • Future NLP applications should focus on improving clinical workflow integration and diagnostic accuracy in oncology.