Dynamic Abandon/Extract Decisions for Failed Cardiac Leads-Reference-Cited by-同舟云学术

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Dynamic Abandon/Extract Decisions for Failed Cardiac Leads

Published:2018-02 Issue:2 Volume:64 Page:633-651
ISSN:0025-1909
Container-title:Management Science
language:en
Short-container-title:Management Science

Author:

Khojandi Anahita¹,Maillart Lisa M.²,Prokopyev Oleg A.²,Roberts Mark S.³,Saba Samir F.⁴

Affiliation:

1. Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, Tennessee 37996

2. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261

3. Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, Pennsylvania 15261

4. Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Strategy and Management

Link

https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2016.2621

Reference41 articles.

1. Should all dysfunctional high-voltage leads be extracted? Results of a single-centre long-term registry

2. Current practice in transvenous lead extraction: a European Heart Rhythm Association EP Network Survey

3. Transvenous removal of pacing and implantable cardiac defibrillating leads using single sheath mechanical dilatation and multiple venous approaches: high success rate and safety in more than 2000 leads

4. Pacemaker and ICD leads: Strategies for long-term management

5. Yes we can! But should we? Lead extraction for superfluous pacemaker and implanted cardioverter-defibrillator leads

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1. When pacing or defibrillator leads become redundant: Extract or abandon?;Heart Rhythm;2024-07

2. Robust condition‐based production and maintenance planning for degradation management;Production and Operations Management;2023-09-21

3. Bias in Reinforcement Learning: A Review in Healthcare Applications;ACM Computing Surveys;2023-09-15

4. Two-stage distributionally robust optimization for joint system design and maintenance scheduling in high-consequence systems;IISE Transactions;2023-08-01

5. A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction;INFORMS Journal on Computing;2022-07

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