Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.diabres.2022.109237
DC Field | Value | |
---|---|---|
dc.title | Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach | |
dc.contributor.author | Kumar, M. | |
dc.contributor.author | Chen, L. | |
dc.contributor.author | Tan, K. | |
dc.contributor.author | Ang, L.T. | |
dc.contributor.author | Ho, C. | |
dc.contributor.author | Wong, G. | |
dc.contributor.author | Soh, S.E. | |
dc.contributor.author | Tan, K.H. | |
dc.contributor.author | Chan, J.K.Y. | |
dc.contributor.author | Godfrey, K.M. | |
dc.contributor.author | Chan, S.-Y. | |
dc.contributor.author | Chong, M.F.F. | |
dc.contributor.author | Connolly, J.E. | |
dc.contributor.author | Chong, Y.S. | |
dc.contributor.author | Eriksson, J.G. | |
dc.contributor.author | Feng, M. | |
dc.contributor.author | Karnani, N. | |
dc.date.accessioned | 2022-08-02T01:10:42Z | |
dc.date.available | 2022-08-02T01:10:42Z | |
dc.date.issued | 2022-02-03 | |
dc.identifier.citation | Kumar, M., Chen, L., Tan, K., Ang, L.T., Ho, C., Wong, G., Soh, S.E., Tan, K.H., Chan, J.K.Y., Godfrey, K.M., Chan, S.-Y., Chong, M.F.F., Connolly, J.E., Chong, Y.S., Eriksson, J.G., Feng, M., Karnani, N. (2022-02-03). Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach. Diabetes Research and Clinical Practice 185 : 109237. ScholarBank@NUS Repository. https://doi.org/10.1016/j.diabres.2022.109237 | |
dc.identifier.issn | 0168-8227 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/229699 | |
dc.description.abstract | Aims: The heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model. Methods: Data from 909 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes. Results: UK NICE guidelines showed poor predictability in Singaporean women [AUC:0.60 (95% CI 0.51, 0.70)]. The non-invasive predictive model comprising of 4 non-invasive factors: mean arterial blood pressure in first trimester, age, ethnicity and previous history of GDM, greatly outperformed [AUC:0.82 (95% CI 0.71, 0.93)] the UK NICE guidelines. Conclusions: The UK NICE guidelines may be insufficient to assess GDM risk in Asian women. Our non-invasive predictive model outperforms the current state-of-the-art machine learning models to predict GDM, is easily accessible and can be an effective approach to minimize the economic burden of universal testing & GDM associated healthcare in Asian populations. © 2022 Elsevier B.V. | |
dc.publisher | Elsevier Ireland Ltd | |
dc.subject | Asian populations | |
dc.subject | Gestational Diabetes Mellitus | |
dc.subject | Heterogeneity | |
dc.subject | Machine Learning | |
dc.subject | Non-Invasive | |
dc.subject | UK NICE | |
dc.type | Article | |
dc.contributor.department | DEAN'S OFFICE (MEDICINE) | |
dc.contributor.department | BIOCHEMISTRY | |
dc.contributor.department | PAEDIATRICS | |
dc.contributor.department | DUKE-NUS MEDICAL SCHOOL | |
dc.contributor.department | OBSTETRICS & GYNAECOLOGY | |
dc.contributor.department | SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH | |
dc.description.doi | 10.1016/j.diabres.2022.109237 | |
dc.description.sourcetitle | Diabetes Research and Clinical Practice | |
dc.description.volume | 185 | |
dc.description.page | 109237 | |
dc.published.state | Unpublished | |
Appears in Collections: | Staff Publications Elements |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
Figures_20211208.docx | 2.02 MB | Microsoft Word XML | OPEN | None | View/Download | |
Highlights_20211208.docx | 19.11 kB | Microsoft Word XML | OPEN | None | View/Download | |
Manuscript_20211208.docx | 81.27 kB | Microsoft Word XML | OPEN | None | View/Download | |
Supplementary Material_20211208.docx | 19.05 kB | Microsoft Word XML | OPEN | None | View/Download | |
Supplementary Table 1_20211208.docx | 28.11 kB | Microsoft Word XML | OPEN | None | View/Download | |
Tables_20211208.docx | 37.95 kB | Microsoft Word XML | OPEN | None | View/Download |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.