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Computational phenotyping of obstructive airway diseases : protocol for a systematic review

Bashir, Muwada Bashir Awad ; Basna, Rani LU orcid ; Zhang, Guo Qiang ; Backman, Helena ; Lindberg, Anne LU ; Ekerljung, Linda ; Axelsson, Malin LU ; Hedman, Linnea ; Vanfleteren, Lowie and Lundbäck, Bo , et al. (2022) In Systematic Reviews 11(1).
Abstract

Background: Over the last decade, computational sciences have contributed immensely to characterization of phenotypes of airway diseases, but it is difficult to compare derived phenotypes across studies, perhaps as a result of the different decisions that fed into these phenotyping exercises. We aim to perform a systematic review of studies using computational approaches to phenotype obstructive airway diseases in children and adults. Methods and analysis: We will search PubMed, Embase, Scopus, Web of Science, and Google Scholar for papers published between 2010 and 2020. Conferences proceedings, reference list of included papers, and experts will form additional sources of literature. We will include observational epidemiological... (More)

Background: Over the last decade, computational sciences have contributed immensely to characterization of phenotypes of airway diseases, but it is difficult to compare derived phenotypes across studies, perhaps as a result of the different decisions that fed into these phenotyping exercises. We aim to perform a systematic review of studies using computational approaches to phenotype obstructive airway diseases in children and adults. Methods and analysis: We will search PubMed, Embase, Scopus, Web of Science, and Google Scholar for papers published between 2010 and 2020. Conferences proceedings, reference list of included papers, and experts will form additional sources of literature. We will include observational epidemiological studies that used a computational approach to derive phenotypes of chronic airway diseases, whether in a general population or in a clinical setting. Two reviewers will independently screen the retrieved studies for eligibility, extract relevant data, and perform quality appraisal of included studies. A third reviewer will arbitrate any disagreements in these processes. Quality appraisal of the studies will be undertaken using the Effective Public Health Practice Project quality assessment tool. We will use summary tables to describe the included studies. We will narratively synthesize the generated evidence, providing critical assessment of the populations, variables, and computational approaches used in deriving the phenotypes across studies Conclusion: As progress continues to be made in the area of computational phenotyping of chronic obstructive airway diseases, this systematic review, the first on this topic, will provide the state of the art on the field and highlight important perspectives for future works. Ethics and dissemination: No ethical approval is needed for this work is based only on the published literature and does not involve collection of any primary or human data. reporting: Systematic review registration: PROSPERO CRD42020164898.

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publishing date
type
Contribution to journal
publication status
published
subject
keywords
Airway disease, Asthma, Clustering, Computation, COPD, Machine learning, Phenotype, Systematic review
in
Systematic Reviews
volume
11
issue
1
article number
216
publisher
BioMed Central (BMC)
external identifiers
  • scopus:85139812629
  • pmid:36229872
ISSN
2046-4053
DOI
10.1186/s13643-022-02078-0
language
English
LU publication?
no
additional info
Publisher Copyright: © 2022, The Author(s).
id
b67cfd5e-080c-4928-81fe-d47282a70b8e
date added to LUP
2024-05-31 18:18:03
date last changed
2024-06-18 13:27:59
@article{b67cfd5e-080c-4928-81fe-d47282a70b8e,
  abstract     = {{<p>Background: Over the last decade, computational sciences have contributed immensely to characterization of phenotypes of airway diseases, but it is difficult to compare derived phenotypes across studies, perhaps as a result of the different decisions that fed into these phenotyping exercises. We aim to perform a systematic review of studies using computational approaches to phenotype obstructive airway diseases in children and adults. Methods and analysis: We will search PubMed, Embase, Scopus, Web of Science, and Google Scholar for papers published between 2010 and 2020. Conferences proceedings, reference list of included papers, and experts will form additional sources of literature. We will include observational epidemiological studies that used a computational approach to derive phenotypes of chronic airway diseases, whether in a general population or in a clinical setting. Two reviewers will independently screen the retrieved studies for eligibility, extract relevant data, and perform quality appraisal of included studies. A third reviewer will arbitrate any disagreements in these processes. Quality appraisal of the studies will be undertaken using the Effective Public Health Practice Project quality assessment tool. We will use summary tables to describe the included studies. We will narratively synthesize the generated evidence, providing critical assessment of the populations, variables, and computational approaches used in deriving the phenotypes across studies Conclusion: As progress continues to be made in the area of computational phenotyping of chronic obstructive airway diseases, this systematic review, the first on this topic, will provide the state of the art on the field and highlight important perspectives for future works. Ethics and dissemination: No ethical approval is needed for this work is based only on the published literature and does not involve collection of any primary or human data. reporting: Systematic review registration: PROSPERO CRD42020164898.</p>}},
  author       = {{Bashir, Muwada Bashir Awad and Basna, Rani and Zhang, Guo Qiang and Backman, Helena and Lindberg, Anne and Ekerljung, Linda and Axelsson, Malin and Hedman, Linnea and Vanfleteren, Lowie and Lundbäck, Bo and Rönmark, Eva and Nwaru, Bright I.}},
  issn         = {{2046-4053}},
  keywords     = {{Airway disease; Asthma; Clustering; Computation; COPD; Machine learning; Phenotype; Systematic review}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Systematic Reviews}},
  title        = {{Computational phenotyping of obstructive airway diseases : protocol for a systematic review}},
  url          = {{http://dx.doi.org/10.1186/s13643-022-02078-0}},
  doi          = {{10.1186/s13643-022-02078-0}},
  volume       = {{11}},
  year         = {{2022}},
}