| Title: | Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes |
| Journal: | Diagnostics |
| Published: | 31 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38893680/ |
| DOI: | https://doi.org/10.3390/diagnostics14111152 |
| URL: | https://www.mdpi.com/2075-4418/14/11/1152/pdf?version=1717134815 |
Publication 11946
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes.</p>
9 Authors
- Robert P Adelson
- Anurag Garikipati
- Yunfan Zhou
- Madalina Ciobanu
- Ken Tawara
- Gina Barnes
- Navan Preet Singh
- Qingqing Mao
- Ritankar Das
1 Application
| Application ID | Title |
|---|---|
| 94816 | Early identification of chronic non-communicable diseases and disorders using machine learning |
Enabling scientific discoveries that improve human health