Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

481
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
481

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Information extraction for prognostic stage prediction from breast cancer medical records using NLP and ML.

Medical & biological engineering & computing·2021
See all related articles

Related Experiment Video

Updated: Oct 26, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

288

Prognostic elements extraction from documents to detect prognostic stage.

Pratiksha R Deshmukh1,2, Rashmi Phalnikar1

  • 1School of Computer Engineering and Technology, MIT World Peace University, Pune, India.

Computer Methods in Biomechanics and Biomedical Engineering
|July 28, 2021
PubMed
Summary

This study predicts breast cancer prognostic stage using natural language processing and fuzzy decision trees. The novel method achieved high accuracy, improving cancer staging and patient prognosis.

Keywords:
Prognostic stageanatomic factoranatomic stagebiologic factorfuzzy decision treemedical reportsnatural language processing

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.2K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.2K

Related Experiment Videos

Last Updated: Oct 26, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

288
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.2K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.2K

Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Prognostic staging is crucial for determining optimal breast cancer treatment strategies.
  • Accurate staging relies on extracting complex clinical and pathological information from medical records.
  • Existing methods face challenges in processing unstructured text data from diverse healthcare settings.

Purpose of the Study:

  • To develop and validate a novel method for predicting breast cancer prognostic stage from unstructured medical records.
  • To assess the accuracy of the proposed method across different geographical regions (rural and urban).
  • To establish a generalized approach for cancer staging applicable to various medical institutions.

Main Methods:

  • A dataset of 465 pathological and clinical breast cancer reports from Indian institutions was utilized.
  • A hybrid approach combining Natural Language Processing (NLP) and Fuzzy Decision Trees (FDT) was employed.
  • Anatomic and biologic factors were extracted from unstructured text data for prognostic stage prediction.

Main Results:

  • The combined NLP and FDT approach demonstrated high accuracy in extracting relevant factors.
  • The prognostic stage prediction achieved an average accuracy of 93% in rural regions and 83% in urban regions.
  • The method proved effective in processing data from diverse medical institutions and regional areas.

Conclusions:

  • The proposed NLP and FDT method offers a highly accurate and generalized approach to breast cancer prognostic staging.
  • This research has the potential to significantly improve breast cancer prognosis by enabling more precise treatment decisions.
  • The study highlights the effectiveness of advanced computational techniques in extracting valuable clinical insights from unstructured medical data.