Computational phenotyping of obstructive airway diseases: protocol for a systematic review Show others and affiliations
2022 (English) In: Systematic Reviews, E-ISSN 2046-4053, Vol. 11, no 1, article id 216Article, review/survey (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages Springer Nature, 2022. Vol. 11, no 1, article id 216
Keywords [en]
Airway disease, Asthma, Clustering, Computation, COPD, Machine learning, Phenotype, Systematic review
National Category
Respiratory Medicine and Allergy
Research subject Nursing
Identifiers URN: urn:nbn:se:ltu:diva-93738 DOI: 10.1186/s13643-022-02078-0 ISI: 000867673700003 PubMedID: 36229872 Scopus ID: 2-s2.0-85139812629 OAI: oai:DiVA.org:ltu-93738 DiVA, id: diva2:1708954
Funder Swedish Research Council Swedish Asthma and Allergy Association Region Västra Götaland Region Västerbotten Swedish Heart Lung Foundation Knut and Alice Wallenberg Foundation Umeå University University of Gothenburg Norrbotten County Council
Note Validerad;2022;Nivå 2;2022-11-07 (hanlid);
Funder: Herman Krefting Foundation for Asthma and Allergy Research; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg
2022-11-072022-11-072022-11-17 Bibliographically approved