Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial.
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 guid...
| Publicado en: | Medical Decision Making Vol. 43; no. 7/8; pp. 760 - 774 |
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| Autores principales: | , , , |
| Formato: | computer program equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Oct/Nov2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173439441&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173439441 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0272989X DKI jtl: Medical Decision Making issn: 0272989X maglogo: Y pubinfo: dt: Oct/Nov2023 vid: 43 iid: 7/8 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 173439441 165107930 173439441 173439441 10.1177/0272989X231188027 173439441 ppf: 760 ppct: 14 formats: tig: atl: Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial. aug: au: Leung, K. H. Benjamin Yousefi, Nasrin Chan, Timothy C. Y. Bayoumi, Ahmed M. affil: Scottish Ambulance Service, Edinburgh, Scotland, UK sug: subj: Decision Making, Clinical Software Utilization Health Care Industry Teaching Methods Human Health Resource Allocation Mathematics Program Implementation Algorithms ab: 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. Highlights: This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python. Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided. pubtype: Academic Journal doctype: computer program equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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