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Related Concept Videos

SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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Related Experiment Video

Updated: Dec 24, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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A decision support system for mammography reports interpretation.

Marzieh Esmaeili1,2, Seyed Mohammad Ayyoubzadeh1,2, Nasrin Ahmadinejad3,4

  • 11Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, 3rd Floor, No #17, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran.

Health Information Science and Systems
|April 8, 2020
PubMed
Summary

This study developed a Clinical Decision Support System (CDSS) using data mining to aid breast cancer diagnosis from mammography reports. The K-nearest neighbor (K-NN) model showed high accuracy, while Random Forest offered the best sensitivity for predicting biopsy needs.

Keywords:
BI-RADSBreast cancerCDSSData miningMammography report

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Area of Science:

  • Medical Informatics
  • Data Mining in Healthcare
  • Breast Cancer Diagnostics

Background:

  • Mammography is crucial for breast cancer diagnosis, but interpreting reports poses challenges for clinicians.
  • Accurate decision-making based on mammography findings is essential for timely patient management.

Purpose of the Study:

  • To address challenges in mammography report interpretation.
  • To propose a Clinical Decision Support System (CDSS) utilizing data mining techniques.
  • To assist clinicians in making informed decisions regarding breast cancer diagnosis.

Main Methods:

  • Collected 2441 mammography reports for analysis.
  • Developed code to transform reports into a structured dataset.
  • Applied Random Forest, Naïve Bayes, K-nearest neighbor (K-NN), and Deep Learning classifiers.
  • Evaluated models using cross-validation, measuring Area Under Curve (AUC), accuracy, sensitivity, and specificity.

Main Results:

  • Mammography type, mass, and calcification features were key for decision-making.
  • The K-nearest neighbor (K-NN) model achieved the highest accuracy (84.06%) and specificity (84.72%).
  • The Random Forest classifier demonstrated the best sensitivity (87.74%) and Area Under Curve (AUC) (0.905).

Conclusions:

  • Data mining approaches effectively support decisions on biopsy referrals from mammography reports.
  • The developed CDSS can assist radiologists, particularly those with less experience.
  • This system enhances the interpretation of mammography reports for improved clinical outcomes.