The online lecture will be conducted by Dr. Martijn Schuemie. Dr. Martijn Schuemie is a Research Fellow at Johnson & Johnson, and a visiting scholar at the Biostatistics Department at UCLA. He focuses on development, evaluation, and implementation of methods for real-world evidence. He leads several core workgroups in the Observational Health Data Science and Informatics (OHDSI), for methods research and open-source software development.
Dr. Martijn Schuemie earned his Master's degree in Economics, specializing in Information Management, and subsequently obtained his PhD in Computer Science. His doctoral research was centered on the development of virtual reality systems for the treatment of phobias, with a focus on optimizing human-computer interaction. His academic tenure commenced at the Erasmus University Medical Center in Rotterdam, where he initially delved into the integration of text mining with scientific literature to bolster molecular biology research. His scope of work later expanded to pharmacoepidemiology, and he played a pivotal role in the EU-ADR project, which aimed to create a drug safety signal detection system utilizing observational data at the population level. In 2012, Dr. Schuemie was distinguished with a one-year fellowship from the FDA and engaged extensively as an Observational Medical Outcomes Partnership (OMOP) researcher at Columbia University.
In 2013, Dr. Schuemie transitioned into the private sector, joining Johnson & Johnson where he furthered his research within OMOP and later in Observational Health Data Science and Informatics (OHDSI). His academic contributions continued as he served as an honorary assistant professor at Hong Kong University in 2014 and 2015, and he is currently engaged as a visiting scholar at the Department of Biostatistics at UCLA, in a virtual capacity. He is the creator of the White Rabbit, Rabbit in a Hat, and Usagi tools, and has co-led the compilation of the Book of OHDSI. At present, Dr. Schuemie is at the helm of three OHDSI workgroups: the Health Analytics Data-to-Evidence Suite (HADES), Methods Research, and Generative AI and Analytics. His principal research interests are in methodological development and evaluation, particularly the application of negative controls in assessing the validity of observational studies. His speech will provoke people to rethink how to better utilize real-world data to improve the pharmaceutical industry and medical evidence, bringing profound implications for the future of healthcare and drug development.
Date: May 23, 2024
Time: 3:10 PM to 4:00 PM
Registration URL:https://forms.gle/
The online link will be provided after successful registration.
※ Prior registration is required. Please register promptly.
※ Target Audience: all colleagues from the Taipei Medical System
※ Faculty members who attend the lecture will receive teacher continuing education hours and staff training for 1 hour respectively.
※ For any inquiries, please email a03643@tmu.edu.tw.
