Ayvaci, Mehmet U. S.

Permanent URI for this collection

Mehmet Ayvaci is an Associate Professor of Management. His research interests include:

  • Healthcare Analytics
  • Economics of Information Systems
  • Healthcare Operations Management
  • Medical Decision Making
  • Algorithmic Decision Making
  • Digital Vulnerabilities
  • Patient-Oriented Technologies
  • Decisions in Breast Cancer Diagnosis and Transplantation
  • Econometrics
  • Markov Decision Processes
  • Game Theory
  • Machine Learning
  • Medical Informatics
  • Economics of Health Information and Technology
  • Decision Theory

ORCID page

Browse

Recent Submissions

Now showing 1 - 1 of 1
  • Item
    When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis
    (INFORMS: Institute for Operations Research and the Management Sciences, 2018-12-20) Ahsen, M. E.; Ayvaci, Mehmet Ulvi Saygi; Raghunathan, Srinivasan; 0000-0001-6997-1639 (Ayvaci, MUS); Ayvaci, Mehmet Ulvi Saygi; Raghunathan, Srinivasan
    When algorithms use data generated by human beings, they inherit the errors stemming from human biases, which likely diminishes their performance. We examine the design and value of a bias-aware linear classification algorithm that accounts for bias in input data, using breast cancer diagnosis as our specific setting. In this context, a referring physician makes a follow-up recommendation to a patient based on two inputs: the patient's clinical-risk information and the radiologist's mammogram assessment. Critically, the radiologist's assessment could be biased by the clinical-risk information, which in turn can negatively affect the referring physician's performance. Thus, a bias-aware algorithm has the potential to be of significant value if integrated into a clinical decision support system used by the referring physician. We develop and show that a bias-aware algorithm can eliminate the adverse impact of bias if the error in the mammogram assessment due to radiologist's bias has no variance. On the other hand, in the presence of error variance, the adverse impact of bias can be mitigated, but not eliminated, by the bias-aware algorithm. The bias-aware algorithm assigns less (more) weight to the clinicalrisk information (radiologist's mammogram assessment) when the mean error increases (decreases), but the reverse happens when the error variance increases. Using point estimates obtained from mammography practice and the medical literature, we show that the bias-aware algorithm can significantly improve the expected patient life years or the accuracy of decisions based on mammography. © 2019, INFORMS.

Works in Treasures @ UT Dallas are made available exclusively for educational purposes such as research or instruction. Literary rights, including copyright for published works held by the creator(s) or their heirs, or other third parties may apply. All rights are reserved unless otherwise indicated by the copyright owner(s).