Dr Areti Manataki is a Lecturer in Computer Science at the University of St Andrews. In her research, she employs artificial intelligence methods to improve the delivery of healthcare services. Her focus is on data-driven optimisation of patient flow and improvement of care pathways for patients with long-term conditions. This includes mining surgical patient flow patterns with the use of machine learning, analysing electronic health record workflow with the use of process mining, and modelling HIV care pathways with the use of formal methods. Areti has recently developed interests in advanced data visualisation, in particular in the healthcare domain. Given the poor health literacy of the general population and the complexity of medical data, Areti thinks that there is a great opportunity for dataviz to help patients, populations and clinicians to better understand and ultimately improve their health and care. Areti has extensive experience in leading a range of innovative health data science and computing courses, teaching undergraduate, postgraduate and professional audiences how to capitalise on the power of data to enhance human health. This includes the hugely successful Code Yourself! and ¡A Programar! courses on Coursera, which have attracted over 500,000 learners worldwide. Prior to joining the University of St Andrews, Areti worked as a Teaching and Research Fellow and as a Senior Researcher at the University of Edinburgh, where she also obtained her PhD.
- Rohani, N, Gal, K, Gallagher, M & Manataki, A 2023, Discovering students’ learning strategies in a visual programming MOOC through process mining techniques. in M Montali, A Senderovich & M Weidlich (eds), Process mining workshops: ICPM 2022 international workshops, Bozen-Bolzano, Italy, October 23–28, 2022, revised selected papers. Lecture notes in business information processing, vol. 468, Springer Science and Business Media B.V., Cham, pp. 539-551, 1st International Workshop “Education meets Process Mining" (EduPM 2022), part of the International Conference on Process Mining (ICPM 2022), Bozen-Bolzano, Italy, 24/10/22. https://doi.org/10.1007/978-3-031-27815-0_39
- Rohani, N, Gal, K, Gallagher, M & Manataki, A 2023, Early prediction of student performance in a health data science MOOC. in M Feng, T Käser & P Talukdar (eds), Proceedings of the 16th international conference on educational data mining (EDM 2023). International Educational Data Mining Society, Online, pp. 325–333, International Conference on Educational Data Mining (EDM 2023), Bengaluru, India, 11/07/23. https://doi.org/10.5281/zenodo.8115721
- Olling Back, C, Manataki, A, Papanastasiou, A & Harrison, E 2021, Stochastic workflow modeling in a surgical ward: towards simulating and predicting patient flow. in X Ye, F Soares, E De Maria, P Gómez Vilda, F Cabitza, A Fred & H Gamboa (eds), Biomedical Engineering Systems and Technologies: 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1400, Springer, Cham, pp. 565-591. https://doi.org/10.1007/978-3-030-72379-8_28
- Olling Back, C, Manataki, A & Harrison, E 2020, Mining patient flow patterns in a surgical ward. in Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies. vol. 5, SciTePress, pp. 273-283, 13th International Conference on Health Informatics, part of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, Valetta, Malta, 24/02/20. https://doi.org/10.5220/0009181302730283