Computational phenotyping of obstructive airway diseases: protocol for a systematic review
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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Muwada Bashir Awad Bashir [verfasserIn] Rani Basna [verfasserIn] Guo-Qiang Zhang [verfasserIn] Helena Backman [verfasserIn] Anne Lindberg [verfasserIn] Linda Ekerljung [verfasserIn] Malin Axelsson [verfasserIn] Linnea Hedman [verfasserIn] Lowie Vanfleteren [verfasserIn] Bo Lundbäck [verfasserIn] Eva Rönmark [verfasserIn] Bright I. Nwaru [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
In: Systematic Reviews - BMC, 2012, 11(2022), 1, Seite 5 |
|---|---|
| Übergeordnetes Werk: |
volume:11 ; year:2022 ; number:1 ; pages:5 |
| Links: |
|---|
| DOI / URN: |
10.1186/s13643-022-02078-0 |
|---|
| Katalog-ID: |
DOAJ086952331 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ086952331 | ||
| 003 | DE-627 | ||
| 005 | 20230502075100.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230311s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/s13643-022-02078-0 |2 doi | |
| 035 | |a (DE-627)DOAJ086952331 | ||
| 035 | |a (DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 0 | |a Muwada Bashir Awad Bashir |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Computational phenotyping of obstructive airway diseases: protocol for a systematic review |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a 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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 | ||
| 650 | 4 | |a Airway disease | |
| 650 | 4 | |a Asthma | |
| 650 | 4 | |a Clustering | |
| 650 | 4 | |a COPD | |
| 650 | 4 | |a Computation | |
| 650 | 4 | |a Machine learning | |
| 653 | 0 | |a Medicine | |
| 653 | 0 | |a R | |
| 700 | 0 | |a Rani Basna |e verfasserin |4 aut | |
| 700 | 0 | |a Guo-Qiang Zhang |e verfasserin |4 aut | |
| 700 | 0 | |a Helena Backman |e verfasserin |4 aut | |
| 700 | 0 | |a Anne Lindberg |e verfasserin |4 aut | |
| 700 | 0 | |a Linda Ekerljung |e verfasserin |4 aut | |
| 700 | 0 | |a Malin Axelsson |e verfasserin |4 aut | |
| 700 | 0 | |a Linnea Hedman |e verfasserin |4 aut | |
| 700 | 0 | |a Lowie Vanfleteren |e verfasserin |4 aut | |
| 700 | 0 | |a Bo Lundbäck |e verfasserin |4 aut | |
| 700 | 0 | |a Eva Rönmark |e verfasserin |4 aut | |
| 700 | 0 | |a Bright I. Nwaru |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t Systematic Reviews |d BMC, 2012 |g 11(2022), 1, Seite 5 |w (DE-627)718627210 |w (DE-600)2662257-9 |x 20464053 |7 nnns |
| 773 | 1 | 8 | |g volume:11 |g year:2022 |g number:1 |g pages:5 |
| 856 | 4 | 0 | |u https://doi.org/10.1186/s13643-022-02078-0 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64 |z kostenfrei |
| 856 | 4 | 0 | |u https://doi.org/10.1186/s13643-022-02078-0 |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/2046-4053 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_206 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_4012 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4367 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 11 |j 2022 |e 1 |h 5 | ||
| author_variant |
m b a b mbab r b rb g q z gqz h b hb a l al l e le m a ma l h lh l v lv b l bl e r er b i n bin |
|---|---|
| matchkey_str |
article:20464053:2022----::opttoapeoyigfbtutvaradsaepoo |
| hierarchy_sort_str |
2022 |
| publishDate |
2022 |
| allfields |
10.1186/s13643-022-02078-0 doi (DE-627)DOAJ086952331 (DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64 DE-627 ger DE-627 rakwb eng Muwada Bashir Awad Bashir verfasserin aut Computational phenotyping of obstructive airway diseases: protocol for a systematic review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 Airway disease Asthma Clustering COPD Computation Machine learning Medicine R Rani Basna verfasserin aut Guo-Qiang Zhang verfasserin aut Helena Backman verfasserin aut Anne Lindberg verfasserin aut Linda Ekerljung verfasserin aut Malin Axelsson verfasserin aut Linnea Hedman verfasserin aut Lowie Vanfleteren verfasserin aut Bo Lundbäck verfasserin aut Eva Rönmark verfasserin aut Bright I. Nwaru verfasserin aut In Systematic Reviews BMC, 2012 11(2022), 1, Seite 5 (DE-627)718627210 (DE-600)2662257-9 20464053 nnns volume:11 year:2022 number:1 pages:5 https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64 kostenfrei https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/toc/2046-4053 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 5 |
| spelling |
10.1186/s13643-022-02078-0 doi (DE-627)DOAJ086952331 (DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64 DE-627 ger DE-627 rakwb eng Muwada Bashir Awad Bashir verfasserin aut Computational phenotyping of obstructive airway diseases: protocol for a systematic review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 Airway disease Asthma Clustering COPD Computation Machine learning Medicine R Rani Basna verfasserin aut Guo-Qiang Zhang verfasserin aut Helena Backman verfasserin aut Anne Lindberg verfasserin aut Linda Ekerljung verfasserin aut Malin Axelsson verfasserin aut Linnea Hedman verfasserin aut Lowie Vanfleteren verfasserin aut Bo Lundbäck verfasserin aut Eva Rönmark verfasserin aut Bright I. Nwaru verfasserin aut In Systematic Reviews BMC, 2012 11(2022), 1, Seite 5 (DE-627)718627210 (DE-600)2662257-9 20464053 nnns volume:11 year:2022 number:1 pages:5 https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64 kostenfrei https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/toc/2046-4053 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 5 |
| allfields_unstemmed |
10.1186/s13643-022-02078-0 doi (DE-627)DOAJ086952331 (DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64 DE-627 ger DE-627 rakwb eng Muwada Bashir Awad Bashir verfasserin aut Computational phenotyping of obstructive airway diseases: protocol for a systematic review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 Airway disease Asthma Clustering COPD Computation Machine learning Medicine R Rani Basna verfasserin aut Guo-Qiang Zhang verfasserin aut Helena Backman verfasserin aut Anne Lindberg verfasserin aut Linda Ekerljung verfasserin aut Malin Axelsson verfasserin aut Linnea Hedman verfasserin aut Lowie Vanfleteren verfasserin aut Bo Lundbäck verfasserin aut Eva Rönmark verfasserin aut Bright I. Nwaru verfasserin aut In Systematic Reviews BMC, 2012 11(2022), 1, Seite 5 (DE-627)718627210 (DE-600)2662257-9 20464053 nnns volume:11 year:2022 number:1 pages:5 https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64 kostenfrei https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/toc/2046-4053 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 5 |
| allfieldsGer |
10.1186/s13643-022-02078-0 doi (DE-627)DOAJ086952331 (DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64 DE-627 ger DE-627 rakwb eng Muwada Bashir Awad Bashir verfasserin aut Computational phenotyping of obstructive airway diseases: protocol for a systematic review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 Airway disease Asthma Clustering COPD Computation Machine learning Medicine R Rani Basna verfasserin aut Guo-Qiang Zhang verfasserin aut Helena Backman verfasserin aut Anne Lindberg verfasserin aut Linda Ekerljung verfasserin aut Malin Axelsson verfasserin aut Linnea Hedman verfasserin aut Lowie Vanfleteren verfasserin aut Bo Lundbäck verfasserin aut Eva Rönmark verfasserin aut Bright I. Nwaru verfasserin aut In Systematic Reviews BMC, 2012 11(2022), 1, Seite 5 (DE-627)718627210 (DE-600)2662257-9 20464053 nnns volume:11 year:2022 number:1 pages:5 https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64 kostenfrei https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/toc/2046-4053 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 5 |
| allfieldsSound |
10.1186/s13643-022-02078-0 doi (DE-627)DOAJ086952331 (DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64 DE-627 ger DE-627 rakwb eng Muwada Bashir Awad Bashir verfasserin aut Computational phenotyping of obstructive airway diseases: protocol for a systematic review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 Airway disease Asthma Clustering COPD Computation Machine learning Medicine R Rani Basna verfasserin aut Guo-Qiang Zhang verfasserin aut Helena Backman verfasserin aut Anne Lindberg verfasserin aut Linda Ekerljung verfasserin aut Malin Axelsson verfasserin aut Linnea Hedman verfasserin aut Lowie Vanfleteren verfasserin aut Bo Lundbäck verfasserin aut Eva Rönmark verfasserin aut Bright I. Nwaru verfasserin aut In Systematic Reviews BMC, 2012 11(2022), 1, Seite 5 (DE-627)718627210 (DE-600)2662257-9 20464053 nnns volume:11 year:2022 number:1 pages:5 https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64 kostenfrei https://doi.org/10.1186/s13643-022-02078-0 kostenfrei https://doaj.org/toc/2046-4053 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 1 5 |
| language |
English |
| source |
In Systematic Reviews 11(2022), 1, Seite 5 volume:11 year:2022 number:1 pages:5 |
| sourceStr |
In Systematic Reviews 11(2022), 1, Seite 5 volume:11 year:2022 number:1 pages:5 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Airway disease Asthma Clustering COPD Computation Machine learning Medicine R |
| isfreeaccess_bool |
true |
| container_title |
Systematic Reviews |
| authorswithroles_txt_mv |
Muwada Bashir Awad Bashir @@aut@@ Rani Basna @@aut@@ Guo-Qiang Zhang @@aut@@ Helena Backman @@aut@@ Anne Lindberg @@aut@@ Linda Ekerljung @@aut@@ Malin Axelsson @@aut@@ Linnea Hedman @@aut@@ Lowie Vanfleteren @@aut@@ Bo Lundbäck @@aut@@ Eva Rönmark @@aut@@ Bright I. Nwaru @@aut@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
718627210 |
| id |
DOAJ086952331 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ086952331</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502075100.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13643-022-02078-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ086952331</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Muwada Bashir Awad Bashir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational phenotyping of obstructive airway diseases: protocol for a systematic review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Airway disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Asthma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COPD</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rani Basna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guo-Qiang Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Helena Backman</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anne Lindberg</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Linda Ekerljung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Malin Axelsson</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Linnea Hedman</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lowie Vanfleteren</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bo Lundbäck</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Eva Rönmark</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bright I. Nwaru</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Systematic Reviews</subfield><subfield code="d">BMC, 2012</subfield><subfield code="g">11(2022), 1, Seite 5</subfield><subfield code="w">(DE-627)718627210</subfield><subfield code="w">(DE-600)2662257-9</subfield><subfield code="x">20464053</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:5</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13643-022-02078-0</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13643-022-02078-0</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2046-4053</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield><subfield code="h">5</subfield></datafield></record></collection>
|
| author |
Muwada Bashir Awad Bashir |
| spellingShingle |
Muwada Bashir Awad Bashir misc Airway disease misc Asthma misc Clustering misc COPD misc Computation misc Machine learning misc Medicine misc R Computational phenotyping of obstructive airway diseases: protocol for a systematic review |
| authorStr |
Muwada Bashir Awad Bashir |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)718627210 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut aut aut aut aut aut |
| collection |
DOAJ |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
20464053 |
| topic_title |
Computational phenotyping of obstructive airway diseases: protocol for a systematic review Airway disease Asthma Clustering COPD Computation Machine learning |
| topic |
misc Airway disease misc Asthma misc Clustering misc COPD misc Computation misc Machine learning misc Medicine misc R |
| topic_unstemmed |
misc Airway disease misc Asthma misc Clustering misc COPD misc Computation misc Machine learning misc Medicine misc R |
| topic_browse |
misc Airway disease misc Asthma misc Clustering misc COPD misc Computation misc Machine learning misc Medicine misc R |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Systematic Reviews |
| hierarchy_parent_id |
718627210 |
| hierarchy_top_title |
Systematic Reviews |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)718627210 (DE-600)2662257-9 |
| title |
Computational phenotyping of obstructive airway diseases: protocol for a systematic review |
| ctrlnum |
(DE-627)DOAJ086952331 (DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64 |
| title_full |
Computational phenotyping of obstructive airway diseases: protocol for a systematic review |
| author_sort |
Muwada Bashir Awad Bashir |
| journal |
Systematic Reviews |
| journalStr |
Systematic Reviews |
| lang_code |
eng |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
txt |
| container_start_page |
5 |
| author_browse |
Muwada Bashir Awad Bashir Rani Basna Guo-Qiang Zhang Helena Backman Anne Lindberg Linda Ekerljung Malin Axelsson Linnea Hedman Lowie Vanfleteren Bo Lundbäck Eva Rönmark Bright I. Nwaru |
| container_volume |
11 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Muwada Bashir Awad Bashir |
| doi_str_mv |
10.1186/s13643-022-02078-0 |
| author2-role |
verfasserin |
| title_sort |
computational phenotyping of obstructive airway diseases: protocol for a systematic review |
| title_auth |
Computational phenotyping of obstructive airway diseases: protocol for a systematic review |
| abstract |
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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 |
| abstractGer |
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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 |
| abstract_unstemmed |
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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
| container_issue |
1 |
| title_short |
Computational phenotyping of obstructive airway diseases: protocol for a systematic review |
| url |
https://doi.org/10.1186/s13643-022-02078-0 https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64 https://doaj.org/toc/2046-4053 |
| remote_bool |
true |
| author2 |
Rani Basna Guo-Qiang Zhang Helena Backman Anne Lindberg Linda Ekerljung Malin Axelsson Linnea Hedman Lowie Vanfleteren Bo Lundbäck Eva Rönmark Bright I. Nwaru |
| author2Str |
Rani Basna Guo-Qiang Zhang Helena Backman Anne Lindberg Linda Ekerljung Malin Axelsson Linnea Hedman Lowie Vanfleteren Bo Lundbäck Eva Rönmark Bright I. Nwaru |
| ppnlink |
718627210 |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.1186/s13643-022-02078-0 |
| up_date |
2024-07-03T23:35:34.586Z |
| _version_ |
1803602870494298112 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ086952331</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502075100.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13643-022-02078-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ086952331</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJcf49ede9a3764bc3aa5b8470f50e7e64</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Muwada Bashir Awad Bashir</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational phenotyping of obstructive airway diseases: protocol for a systematic review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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 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. Registration and reporting Systematic review registration PROSPERO CRD42020164898</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Airway disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Asthma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COPD</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rani Basna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guo-Qiang Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Helena Backman</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anne Lindberg</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Linda Ekerljung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Malin Axelsson</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Linnea Hedman</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lowie Vanfleteren</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bo Lundbäck</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Eva Rönmark</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bright I. Nwaru</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Systematic Reviews</subfield><subfield code="d">BMC, 2012</subfield><subfield code="g">11(2022), 1, Seite 5</subfield><subfield code="w">(DE-627)718627210</subfield><subfield code="w">(DE-600)2662257-9</subfield><subfield code="x">20464053</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:5</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13643-022-02078-0</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/cf49ede9a3764bc3aa5b8470f50e7e64</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s13643-022-02078-0</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2046-4053</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield><subfield code="h">5</subfield></datafield></record></collection>
|
| score |
7.3995934 |