A Framework For Evaluating Synthetic Electronic Health Records
Poster Area
Lecturers: Amira Soliman, Emmanuella Budu, Kobra Etminani
Track: MIE: Posters
Monday 22 May: 1pm-3pm
Tuesday 23 May: 1pm-2pm
Language
English
Seminar type
On site only
Level of knowledge
Advanced
Conference
MIE
Authors
Emmanuella Budu, Amira Soliman, Kobra Etminani, Thorsteinn Rögnvaldsson
Lecturers
Amira Soliman Lecturer
Assistant Professor
Högskolan i Halmstad
My research experience spans distributed systems, data analysis, and machine learning with a focus on interlinked data analysis using graph theory for complex and large systems. My research is directed towards the exploration of new algorithms for supporting the design and implementation of robust and large-scale learning algorithms, especially in healthcare. I believe artificial intelligence and machine learning can offer more personalized and scalable solutions for better information-driven healthcare.
Emmanuella Budu Lecturer
PhD Student
Högskolan i Halmstad
Emmanuella is a PhD student at the Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden, working on the generation and assessment of synthetic Electronic Health Records (EHRs).
Kobra Etminani Lecturer
Associate Professor, Docent
Center for Applied Intelligent Systems Research (CAISR) in Health, Halmstad University
Kobra (Farzaneh) Etminani is an associate Professor, Docent, working at the Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden. She is Deputy Profile Manager for CAISR Health, a Swedish funded research profile. She is also part of the extended management group for Information Driven Care research program (IDC) at Halmstad University.
She manages the Real-World Evidence (RWE) research projects together with Health Data Center (HDC), that includes several research projects together with Region Halland (a regional Swedish healthcare system), analytics companies, and big Pharma.
She has worked on various topics and application areas within Machine Learning (ML), Artificial Intelligence (AI), Data Mining, and Deep Learning (DL) in the last decade, focused on healthcare, district heating, and mobility. Her main research interest is focused on solving real-world problems, which is focused on a healthier society, with the help of AI and ML, if possible and applicable. Her recent research focus is on patient trajectories and eXplainable AI (XAI) in precision healthcare