Anna Markella Antoniadi

Anna Markella Antoniadi was a PhD student in FutureNeuro– an SFI Research Centre- at the School of Computer Science of University College Dublin, Ireland from October 2017 to September 2021. Google Scholar, Researchgate

Scientific Interests: Health Informatics

PhD Thesis: “The Application of Explainable Artificial Intelligence in Modelling Quality of Life and Caregiver Burden in Amyotrophic Lateral Sclerosis to Inform Clinical Decision-Making”

Her PhD thesis provides a thorough description of the application of Machine Learning (ML) in medicine to inform clinical research and develop Clinical Decision Support Systems (CDSS), while focusing on matters of explainability, ethics, data protection and the usability of such models and systems for their incorporation in the clinical workflow. The practical part of this thesis describes the use of ML to predict the well-being of people with Amyotrophic Lateral Sclerosis (ALS) and their informal caregivers, in terms of experienced quality of life and burden. This work aims to uncover the factors that relate to their well-being, and develop a CDSS that informs clinicians so as to personalise the support the affected individuals receive. Part of this work was also a user study to test the CDSS in terms of explainability in the eyes of healthcare professionals.

Secondment: 10-month secondment for CareHD, a Horizon 2020 research program (academic year 2019-2020) .

Education:

  • PhD Computer Science– University College Dublin (2021)
  • MSc Biostatistics– University of Glasgow (2017)
  • BSc Computer Science– Athens University of Economics and Business (2016)

Papers:

  1. Anna Markella Antoniadi, Miriam Galvin, Mark Heverin, Orla Hardiman, and Catherine Mooney. (2021). Prediction of Quality of Life in People with ALS: on the Road Towards Explainable Clinical Decision Support. ACM SIGAPP Applied Computing Review, 21(2), 5-17. https://doi.org/10.1145/3477127.3477128
  2. Anna Markella Antoniadi, Miriam Galvin, Mark Heverin, Orla Hardiman, and Catherine Mooney. (2021). Prediction of Caregiver Quality of Life in Amyotrophic Lateral Sclerosis Using Explainable Machine Learning. Scientific Reports, 11(1), 1-13. https://doi.org/10.1038/s41598-021-91632-2
  3. Antoniadi, A.M.; Du, Y.; Guendouz, Y.; Wei, L.; Mazo, C.; Becker, B.A.; Mooney, C. (2021). Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: a Systematic Review. Applied Sciences, 11(11), 5088. https://doi.org/10.3390/app11115088
  4. Anna Markella Antoniadi, Miriam Galvin, Mark Heverin, Orla Hardiman, and Catherine Mooney. (2021, March). Development of an explainable clinical decision support system for the prediction of patient quality of life in amyotrophic lateral sclerosis. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (pp. 594-602). doi: https://doi.org/10.1145/3412841.3441940
  5. Anna Markella Antoniadi, Miriam Galvin, Mark Heverin, Orla Hardiman, and Catherine Mooney. (2020). Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study. BMJ open, 10(2), e033109. doi: 10.1136/bmjopen-2019-033109

Posters:

  1. Anna Antoniadi, Miriam Galvin, Mark Heverin, Orla Hardiman, and Catherine Mooney. 2020. Identifying Features That Are Predictive of Quality of Life in People With Amyotrophic Lateral Sclerosis. Presented at IEEE ICHI 2020.
  2. Anna Antoniadi, Miriam Galvin, Mark Heverin, Orla Hardiman, and Catherine Mooney. 2020. Using Patient Information for the Prediction of Caregiver Burden in Amyotrophic Lateral Sclerosis. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB ’20), September 21–24, 2020, Virtual Event, USA.ACM, NewYork, NY, USA, 1 page. https://doi.org/10.1145/3388440.3414908
  3. Best Poster Award in ITiCSE ’20:
    C. Mooney, A. Antoniadi, I. Karvelas, L. Salmon, B.A. Becker.  Exploring Sense of Belonging in Computer Science Students. ITiCSE ’20: Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. June 2020 Pages 563. https://doi.org/10.1145/3341525.3393974
  4. A.M. Antoniadi, C. Mooney. The Multi-Dimensional Process of Developing a Clinical Decision Support System using Machine Learning. ACM Celebration of Women in Computing: womENcourage, Rome, Italy, 2019. Link to poster

Computer Science Education:

  • Leader, co-organiser and material-developer at the CS Sparks program in UCD School of Computer Science for the academic year 2019-2020.
  • Material-developer and tutor at the Future You Summer School of UCD Access & Lifelong Learning .

Women in Computer Science:

  • UCD Women@CompSci co-founding and committee member.
  • Awarded IBM Research scholarship to attend the first Gender Equality Workshop at the International Conference on Software Engineering (ICSE) 2018.
  • Awarded scholarship to attend the 6th ACM Celebration of Women in Computing: womENcourage 2019.

Media:

Interview for the Irish Times

 

https://twitter.com/anna_antoniadi