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Related Experiment Video

Updated: Sep 25, 2025

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

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

Published on: January 11, 2020

Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning.

Theodore P Papalexopoulos¹, Dimitris Bertsimas¹, I Glenn Cohen²

¹Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Journal of Law and the Biosciences

|May 2, 2022

Summary

This summary is machine-generated.

This study introduces a framework for allocating scarce medical resources, like organ transplants, balancing efficiency and fairness. It uses a novel tool to help stakeholders make evidence-based policy decisions.

Keywords:

Analytics ethics by design machine learning organ allocation organ transplantation resource allocation

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

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Public Policy
Health Economics
Bioethics

Background:

Distributing scarce medical resources involves complex trade-offs between efficiency and fairness.
Diverse stakeholder values complicate policy development in healthcare allocation.

Purpose of the Study:

To present a general framework for resource allocation policy development.
To illustrate this framework using organ transplant allocation.
To develop an analytical tool for exploring policy outcomes.

Main Methods:

Combining ethical theory, data modeling, and stakeholder input.
Developing a novel analytical tool using machine learning and optimization.
Case study application to organ transplant allocation policies.

Main Results:

The framework facilitates balancing multiple objectives in policy design.
The analytical tool enables efficient exploration of policy outcomes.
Stakeholders can develop evidence-based value judgments on tradeoffs.

Conclusions:

The proposed framework and tool enhance policymaking for scarce resource distribution.
Improved stakeholder engagement leads to more informed and equitable allocation policies.
This approach supports evidence-based decision-making in critical healthcare policy areas.