Yuhan, Anna and Catherine attended the 11th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB 2020), the flagship conference of ACM SIGBio held virtually between Sep 21 and 24, 2020.
Yuhan presented a poster co-authored by John Mehegan, Fionnuala McAuliffe and Catherine Mooney, titled “Prediction of Large for Gestational Infants in Overweight and Obese Women at Approximately 20 Gestational Weeks”. Large for gestational age (LGA) birth is an adverse pregnancy outcome associated with many maternal and perinatal complications, however, there isn’t any established rule to predict LGA in early pregnancy. This poster presents our preliminary work on addressing the prediction of LGA in the second trimester using machine learning. It shows the potential of applying machine learning techniques to assist clinical decision makings to prevent maternal and neonatal morbidity.
[pdf-embedder url=”https://lisda.ucd.ie/wp-content/uploads/2020/09/ACM-BCB-poster.pdf” title=”ACM-BCB poster”]
— Yuhan Du
Anna Markella Antoniadi presented a poster co-authored by Miriam Galvin, Mark Heverin, Orla Hardiman and Catherine Mooney on “Using Patient Information for the Prediction of Caregiver Burden in Amyotrophic Lateral Sclerosis”. Previous work had identified important predictors of caregiver burden using more information. Here the authors aimed to reduce the predictive features to patient information alone in order to explore the possibility of developing a more usable Clinical Decision Support System (CDSS) for the prediction of caregiver burden, following the General Data Protection Regulations (GDPR) data minimisation principle.
[pdf-embedder url=”https://lisda.ucd.ie/wp-content/uploads/2020/09/ACM-BCB-2020-poster.pdf” title=”ACM-BCB 2020 poster”]
-Anna Markella Antoniadi