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Data Science

Data science, a cross-cutting theme, extends across all four research pillars/themes and includes the following research groups:

AI/Machine learning in molecular medicine (Chris Yau); Deep Medicine (Kazem Rahimi); Digital Phenotyping (Chris Nellåker); Epidemiology, Genomics, and Multi-omics (Krina Zondervan, Katy Vincent); AI in placental imaging (Sally Collins) and ultrasound imaging (Aris Papageorgiou); AI in antenatal (Gabriel Jones/Manu Vatish) and labour CTG monitoring (Antoniya Georgieva). Honorary Senior Research Fellow Cecilia Lindgren (BDI/NDPH) supports various programmes including digital phenotyping.

 



Topics in this theme

The Deep Medicine programme combines pioneering data analytic approaches, deep and machine learning techniques, and interdisciplinary collaboration to generate insights that will help tackle some of the major causes of death and disability worldwide.
Deep Medicine
With MuM-PreDiCT we aim to provide deeper understanding of what makes some pregnant women more likely to have two or more long-term health conditions and how this affects them and their children.
MuM-PreDiCT
The placenta is the most indispensable, disposable organ you ever had. It decrees both how well we grow and develop in the womb and our future health well into adulthood. Our research explores two key themes: 1) Imaging the placenta early in pregnancy in order to predict adverse pregnancy outcomes including fetal growth restriction and 2) Diagnosis and management of Placenta Accreta Spectrum Disorder (PAS) - also known as abnormally invasive placenta (AIP).
Placental Imaging