Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial

doi:10.1177/0272989X231188027

2023-07-22

DOI：10.1177/0272989X231188027

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摘要

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Constrained optimization can be used to make decisions aimed at maximizing some quantity in the face of fixed limits, such as resource allocation problems in health where tradeoffs between alternatives are inherent, and has been applied in a variety of health-related applications. This tutorial guides the reader through the process of mathematically formulating a constrained optimization problem, providing intuitive explanations for each component within the problem. We discuss how constrained optimization problems can be implemented using software and provide instructions on how to set up a solution environment using Python and the Gurobi solver engine. We present 2 examples from the existing literature that illustrate different constrained optimization problems in health and provide the reader with Python code used to solve these problems as well as a discussion of results and sensitivity analyses. This tutorial can be used to help readers formulate constrained optimization problems in their own application domains.

Keyword:

constrained optimization

Python tutorial

prescriptive analytics

resource allocation

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期刊

Medical Decision Making

IF:

2.3

论文数:

2.6K

被引数:

6.4K

机构

saint michaels hospital toronto

学者数:

5.9K

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被引数: 12

university of toronto

学者数:

14.8W

论文数: 12.0W

被引数: 165

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