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Related Experiment Video

Updated: Jun 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

How does age affect baseline screening mammography performance measures? A decision model.

John D Keen1, James E Keen

  • 1Department of Radiology, John H. Stroger Jr. Hospital of Cook County, 1901 West Harrison Street, Chicago, IL 60612-9985, USA. jkeen@ccbhs.org

BMC Medical Informatics and Decision Making
|September 23, 2008
PubMed
Summary

Screening mammography performance, including cancer detection and positive predictive values, significantly improves with age. This study models these age-dependent changes to aid informed medical decisions.

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Continued Avoidance of USPSTF Guidelines for Screening Mammography.

Journal of women's health (2002)·2018
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Opportunity cost of annual screening mammography.

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Utilization of Computer-Aided Detection for Digital Screening Mammography in the United States, 2008 to 2016.

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Relative Utility or Marginal Positive Predictive Values Accounting for Overdiagnosis Should Guide Optimal Recall Rates.

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Four Principles to Consider Before Advising Women on Screening Mammography.

Journal of women's health (2002)·2015

Area of Science:

  • Radiology
  • Oncology
  • Biostatistics

Background:

  • Informed medical decision-making for screening mammography requires understanding age-related performance metrics.
  • A decision model was developed to predict age-specific outcomes for baseline mammograms.

Purpose of the Study:

  • To model the age dependence of cancer detection rate, recall rate, and secondary performance measures for screening mammography.
  • To compare baseline screening mammography with no screening.

Main Methods:

  • A decision tree model was constructed for women aged 35-65 using Surveillance Epidemiology and End Results (SEER) data.
  • Population-based estimates for mammography accuracy and diagnostic outcomes were utilized.
  • Radiologist performance variability was incorporated into the model.

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Last Updated: Jun 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Main Results:

  • Cancer detection rate increases with age (1.9/1000 at 40 to 15.1/1000 at 60).
  • Positive predictive values for screening and diagnostic mammograms increase with age.
  • Total intervention and positive biopsy fractions also increase with age.

Conclusions:

  • Breast cancer prevalence and screening mammography performance measures significantly increase with age.
  • The model provides age-specific data to support informed consumer decision-making regarding mammography screening.