Evidence map›Paper›PMID 37397165›Full record

ArticleHealth information science and systems2023

Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm.

Xinyu Pang, Yong-Feng Ge, Kate Wang, Agma J M Traina, Hua Wang

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In one paragraph

Article in Health information science and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xinyu PangGuangdong Technion Israel Institute of Technology, Shantou, China.
Yong-Feng GeInstitute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia.ORCID 0000-0002-5955-6295
Kate WangSchool of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
Agma J M TrainaInstitute of Mathematics and Computer Sciences, University of São Paulo, São Paulo, Brazil.
Hua WangInstitute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.

Indexed as

Cloud healthcare systemDistributed genetic algorithmEvolutionary algorithmPatient assignment

Identifiers

PMID37397165
PMCPMC10307766

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.