Tissot, HC;
Pedebos, LA;
(2021)
Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings.
Journal of Healthcare Informatics Research
, 5
(4)
pp. 359-381.
10.1007/s41666-021-00096-6.
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Abstract
Miscarriages are the most common type of pregnancy loss, mostly occurring in the first 12 weeks of pregnancy. Pregnancy risk assessment aims to quantify evidence to reduce such maternal morbidities, and personalized decision support systems are the cornerstone of high-quality, patient-centered care to improve diagnosis, treatment selection, and risk assessment. However, data sparsity and the increasing number of patient-level observations require more effective forms of representing clinical knowledge to encode known information that enables performing inference and reasoning. Whereas knowledge embedding representation has been widely explored in the open domain data, there are few efforts for its application in the clinical domain. In this study, we contrast differences among multiple embedding strategies, and we demonstrate how these methods can assist in performing risk assessment of miscarriage before and during pregnancy. Our experiments show that simple knowledge embedding approaches that utilize domain-specific metadata perform better than complex embedding strategies, although both can improve results comparatively to a population probabilistic baseline in both AUPRC, F1-score, and a proposed normalized version of these evaluation metrics that better reflects accuracy for unbalanced datasets. Finally, embedding approaches provide evidence about each individual, supporting explainability for its model predictions in such a way that humans understand.
| Type: | Article |
|---|---|
| Title: | Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s41666-021-00096-6 |
| Publisher version: | https://doi.org/10.1007/s41666-021-00096-6 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Risk assessment, Pregnancy, Miscarriages, Machine learning, Knowledge embeddings, Personalized medicine health, Information management, Public healthcare |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10141164 |
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