Yuhan @ The 10th Overseas Returnees Forum in Hunan

Yuhan was invited to the 10th Overseas Returnees Forum on 2-4 Nov, 2023 in Changsha, Hunan, China. At the forum, she met lots of Chinese PhDs who studied overseas, and shared her experience and research with them. She also learnt about the academic and industry positions in Changsha. It was a great event and Changsha is a beautiful city!

Yuhan’s Graduation Day

Congratulations to Dr. Yuhan Du on her PhD graduation!

Graduation day yesterday🎓 Watched the live-streamed ceremony with so much pride and gratitude. It’s been an incredible journey of growth, learning, and unforgettable memories. Big thanks to my supervisors, colleagues, friends and family! Excited for what the future holds! — Dr Yuhan Du

Best Paper Award

Our review paper on XAI in ML-based CDSS won Applied Sciences 2021 best paper award in Section Applied Biosciences and Bioengineering! Huge congratulations to LiSDA!

Yuhan’s PhD Viva

Huge congratulations to Yuhan who successfully passed her PhD Viva on 15 December 2022! And big congratulations to Catherine having her third PhD students finished!

Yuhan’s thesis title is “Explainable Clinical Decision Support for the Prediction of Complications in Pregnancy”.

Many thanks to her external examiner, Dr Dympna O Sullivan, TU Dublin, and internal examiner, Dr Soumyabrata Dev, and to Dr David Lillis for arranging and chairing the viva.

Many thanks to Yuhan’s co-supervisor, Prof Fionnuala McAuliffe, UCD School of Medicine and National Maternity Hospital, and to Yuhan’s RSP, Dr Mark Matthews and Dr Derek Greene, for their support and guidance over the last four years.

The Role of XAI in Advice-Taking from a Clinical Decision Support System

Delighted that our paper titled “The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations” has been published on Applied Sciences.

This paper presents interesting findings from our user study with healthcare practitioners to investigate the role of XAI in a CDSS. It revealed what type of explanations healthcare practitioners need and would like to see, and how explanations affect their decision-making. Check it out at: https://www.mdpi.com/2076-3417/12/20/10323

— Yuhan

You Can Be What You Can See: Role Models in pSTEM

We are proud to celebrate International Day of Women and Girls in Science in UCD today.

UCD is marking International Day of Women and Girls in Science by launching a series of videos to encourage more girls to consider a career in pSTEM. The video series, entitled “Role Models in pSTEM: You Can Be What You Can See”, showcases ten female role models from across Ireland who have studied physics, maths, engineering, or computer science. Three of the videos were revealed at an event in UCD today to mark International Day of Women and Girls in Science.

Commissioned by UCD’s School of Mathematics and Statistics, and School of Computer Science, the full suite of videos will launch in May 2022. The videos will be accompanied by an educational resource for teachers which can be used in schools across Ireland – with a particular focus on DEIS schools – to encourage students to identify their own local role models, while highlighting the varied and exciting career opportunities open to young women in pSTEM.

Leading the project are Dr. Aoibhinn Ní Shúilleabháin, Assistant Professor at the School of Mathematics and Statistics, and Dr. Catherine Mooney, Associate Professor in the School of Computer Science at University College Dublin.

We (Lan Wei, Yuhan Du, Shamima Nasrin Runa) are proud to volunteer and participate in the event to hear the inspiring talks by the female role models.

You can find the videos on our YouTube channel: https://www.youtube.com/channel/UCYrh8Eh848_Ljzfx5BXiE9A
Our temporary webpage (to be updated for the full launch) is here: https://www.ucd.ie/mathstat/rolemodelsinpstem/

XAI for Prediction of Gestational Diabetes

Our paper “An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus” is published in Scientific Reports today.

Gestational Diabetes Mellitus (GDM), a common pregnancy complication associated with many maternal and neonatal consequences, is increased in mothers with overweight and obesity. Interventions initiated early in pregnancy can reduce the rate of GDM in these women, however, untargeted interventions can be costly and time-consuming. We have developed an explainable machine learning-based clinical decision support system (CDSS) to identify at-risk women in need of targeted pregnancy intervention. Maternal characteristics and blood biomarkers at baseline from the PEARS study were used. After appropriate data preparation, synthetic minority oversampling technique and feature selection, five machine learning algorithms were applied with five-fold cross-validated grid search optimising the balanced accuracy. Our models were explained with Shapley additive explanations to increase the trustworthiness and acceptability of the system. We developed multiple models for different use cases: theoretical (AUC-PR 0.485, AUC-ROC 0.792), GDM screening during a normal antenatal visit (AUC-PR 0.208, AUC-ROC 0.659), and remote GDM risk assessment (AUC-PR 0.199, AUC-ROC 0.656). Our models have been implemented as a web server that is publicly available for academic use. Our explainable CDSS demonstrates the potential to assist clinicians in screening at risk patients who may benefit from early pregnancy GDM prevention strategies.

Check it out at: https://rdcu.be/cFqM3


LiSDA @ vGHC21

I (Yuhan Du) attended the Grace Hopper Celebration 2021 on Sep 27 – Oct 1 and presented my poster “Early Prediction of Macrosomia Using Machine Learning”, coauthored with Dr. Catherine Mooney. The poster described our research on predicting fetal macrosomia, which refers to infants born with excessive birth weight. In this study, we developed machine learning models to predict macrosomia in the early second trimester in secundigravid women who had a macrosomic birth history with a novel inclusion of usability. Our work has the potential to assist pregnancy care in a clinical setting.

You can find our poster at: https://vghc21-anitab.ipostersessions.com/Default.aspx?s=FF-75-6E-65-72-A8-15-7C-1A-08-33-9E-11-38-C7-A0

–Yuhan Du

LiSDA @ ACM BCB 2021

I (Yuhan Du) attended the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) https://acm-bcb.org/ that was held online on Aug 1-4, 2021. I presented my poster entitled “Explaining Large-for-Gestational-Age Births: A Random Forest Classifier with a Novel Local Interpretation Method”, coauthored with Dr. Anthony R Rafferty, Prof. Fionnuala M McAuliffe and Dr. Catherine Mooney. In the poster, the authors proposed a novel local interpretation method for a random forest classifier based on feature occurrence frequency in trees that give the same prediction as the random forest classifier. The method shows promising results when applied to a random forest classifier for large-for-gestational-age births.

– Yuhan

LiSDA @ AMIA Informatics Summit 2021

I (Yuhan Du) am proud to present my poster entitled “Prediction of Gestational Diabetes Mellitus in Overweight and Obese Caucasian Women using Machine Learning” coauthored by Professor Fionnuala M McAuliffe and Dr Catherine Mooney at AMIA Informatics Summit 2021 on March 23, 2021.
The poster presents our initial results on predicting gestational diabetes mellitus (GDM) in overweight and obese Caucasian pregnant women. GDM is a common pregnancy complication associated with many maternal and neonatal consequences, and it is increased in risk by overweight and obesity. We applied machine learning to develop prediction models for GDM in overweight and obese Caucasian women to identify at-risk women in need of intervention early in pregnancy. This work was based on maternal characteristics and blood biomarkers at baseline from the PEARS study. After appropriate data preparation and feature selection, five machine learning algorithms were applied with synthetic minority oversampling technique and five-fold cross validation repeated five times optimising the area under precision-recall curve. Our results showed that a support vector machine with polynomial kernel outperformed other algorithms, identifying 44% and 67% of GDM women in early pregnancy at 5% and 10% false positive rate respectively. This work demonstrates the potential of applying machine learning to the prediction of GDM in a clinical setting.

[pdf-embedder url=”https://lisda.ucd.ie/wp-content/uploads/2021/03/PS01_Du.pdf”]


LiSDA @ ACM-BCB 2020

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


Yuhan Du’s Stage Transfer

Very excited to announce that Yuhan Du has passed her PhD stage transfer and progressed to Stage 2 of her PhD.

Great thanks from Yuhan to the LiSDA team for their help along the way. Everyone was very supportive and gave very helpful feedback. Couldn’t have done this without the team.

— Yuhan Du

Teaching Machine Learning Courses in NUI Galway

Between 4/11/2019 and 8/11/2019, Dr Catherine Mooney was invited to teach an introduction to machine learning to the SFI Centre for Research Training in Genomics Data Science in NUI Galway. Yuhan Du and Lan Wei were invited to demonstrate for the lab sessions.

The courses were very informative and well-received. We were glad to know that the students learnt a lot about machine learning and how it could be applied to their research. It was a real pleasure to  meet many staffs and students there. What a great week!

— Yuhan Du