Journal article
Toward a multivariate prediction model of pharmacological treatment for women with gestational diabetes mellitus: Algorithm development and validation
- Abstract:
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Background:
Successful management of gestational diabetes mellitus (GDM) reduces the risk of morbidity in women and newborns. A woman’s blood glucose readings and risk factors are used by clinical staff to make decisions regarding the initiation of pharmacological treatment in women with GDM. Mobile health (mHealth) solutions allow the real-time follow-up of women with GDM and allow timely treatment and management. Machine learning offers the opportunity to quickly analyze large quantities of data to automatically flag women at risk of requiring pharmacological treatment.
Objective: The aim of this study is to assess whether data collected through an mHealth system can be analyzed to automatically evaluate the switch to pharmacological treatment from diet-based management of GDM.
Methods: We collected data from 3029 patients to design a machine learning model that can identify when a woman with GDM needs to switch to medications (insulin or metformin) by analyzing the data related to blood glucose and other risk factors.
Results: Through the analysis of 411,785 blood glucose readings, we designed a machine learning model that can predict the timing of initiation of pharmacological treatment. After 100 experimental repetitions, we obtained an average area under the receiver operating characteristic curve of 0.80 (SD 0.02) and an algorithm that allows the flexibility of setting the operating point rather than relying on a static heuristic method, which is currently used in clinical practice.
Conclusions: Using real-time data collected via an mHealth system may further improve the timeliness of the intervention and potentially improve patient care. Further real-time clinical testing will enable the validation of our algorithm using real-world data.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 909.6KB, Terms of use)
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- Publisher copy:
- 10.2196/21435
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Grant:
- EP/N020774/1
- EP/P009824/1
- Publisher:
- JMIR Publications
- Journal:
- Journal of Medical Internet Research More from this journal
- Volume:
- 23
- Issue:
- 3
- Article number:
- e21435
- Place of publication:
- Canada
- Publication date:
- 2021-03-10
- Acceptance date:
- 2021-01-17
- DOI:
- EISSN:
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1438-8871
- ISSN:
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1439-4456
- Pmid:
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33688832
- Language:
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English
- Keywords:
- Pubs id:
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1167676
- Local pid:
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pubs:1167676
- Deposit date:
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2021-04-30
Terms of use
- Copyright holder:
- C Velardo et al.
- Copyright date:
- 2021
- Rights statement:
- © Carmelo Velardo, David Clifton, Steven Hamblin, Rabia Khan, Lionel Tarassenko, Lucy Mackillop. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.03.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
- Licence:
- CC Attribution (CC BY)
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