Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation

Domaine:

healthcare

Type de record:

paper

Créateur:

LinLauTevGeo

Éditeur:

ope

Hôte:

Abstract Data-driven clinical prediction algorithms are used widely by clinicians. Understanding what factors can impact the performance and fairness of data-driven algorithms is an important step towards achieving equitable healthcare. To investigate the impact of modeling choices on the algorithmic performance and fairness, we make use of a case study to build a prediction algorithm for estimating glomerular filtration rate (GFR) based on the patient’s electronic health record (EHR). We compare three distinct approaches for estimating GFR: CKD-EPI equations, epidemiological models, and EHR-based models. For epidemiological models and EHR-based models, four machine learning models of varying computational complexity (i.e., linear regression, support vector machine, random forest regression, and neural network) were compared. Performance metrics included root mean squared error (RMSE), median difference, and the proportion of GFR estimates within 30% of the measured GFR value (P30). Differential performance between non-African American and African American group was used to assess algorithmic fairness with respect to race. Our study showed that the variable race had a negligible effect on error, accuracy, and differential performance. Furthermore, including more relevant clinical features (e.g., common comorbidities of chronic kidney disease) and using more complex machine learning models, namely random forest regression, significantly lowered the estimation error of GFR. However, the difference in performance between African American and non-African American patients did not decrease, where the estimation error for African American patients remained consistently higher than non-African American patients, indicating that more objective patient characteristics should be discovered and included to improve algorithm performance.

Visit

doi.org

Similaires

EVALUATING THE PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR PREDICTING METEOROLOGICAL PARAMETERSFairness in Predicting Cancer Mortality Across Racial SubgroupsEvaluating the Performance of GPM IMERG Products for Precipitation Estimation in the Volta River Basin of GhanaAccept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization ApproachesEvaluating Racial Bias in Large Language Models: The Necessity for "SMOKY"Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups

EVALUATING THE PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR PREDICTING METEOROLOGICAL PARAMETERS

Artificial learning techniques are currently used for weather and climate forecasting, etc. In this

Fairness in Predicting Cancer Mortality Across Racial Subgroups

Importance Machine learning has potential to transform cancer care by helping clinicians prioritize

Evaluating the Performance of GPM IMERG Products for Precipitation Estimation in the Volta River Basin of Ghana

Abstract The Volta River basin in West Africa, covering a substantial area of Gh

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches

Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks, such as

Evaluating Racial Bias in Large Language Models: The Necessity for "SMOKY"

This paper evaluates the understanding and biases of large language models (LLMs) regarding

Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups

Automated gender classification has important applications in many domains, such as demographic rese