This month we are pleased to introduce DHIN member Dr Jingjing You. Jingjing is a researcher at Save Sight Institute and she shares with us her work and the benefits of digital health.
Please tell us a little bit about yourself
I am Jingjing, I am currently working at Save Sight Institute, University of Sydney, despite my earlier research on Prostate Cancer. I love my own research, but also love to explore new ideas and talk to researchers outside my field. As a research only academic, and maybe for all academics, I have little time to myself. How to balance life and work is a constant struggle. However, one activity I enjoy a lot, and always do, is to play piano with my son. We are currently leaning the “heart and soul” duet. It has been 1 month with very slow progress. Nevertheless, I am not in any hurry, the learning process is also enjoyable.
How do you define digital health?
Digital health is the future. COVID-19 proves it. If we could have everything digital, there will be minimal impact on hospital systems during a pandemic situation. Especially, I think digital health is strongly linked with machine learning which allows an accurate performance of programs in diagnosing, treating and monitoring diseases.
What do you think will enable digital health projects and innovations to succeed?
Collaboration. A strong collaboration among researchers, clinicians, program developers, patients, hospital operational staff, and policy markers. In the context of machine learning, it is essential to have people with machine learning knowledge, but also people to understand the key questions that need to be solved and how the final product can be used. Together, a suitable machine learning program can be developed and applied to solve real world problems. During the collaboration, innovative ideas and approaches will be generated.
What do you think are the biggest challenges facing digital health at the moment?
Finding the right people and being able to communicate with people with various research backgrounds. Digital health is key at the moment and my experience is that many clinicians and researchers outside the digital health field would love to use it; however it is also very new therefore it is hard to know where to start or if digital health approach applies to the project. I think people have to be persistent with their ideas. I started my machine learning project by self-learning to have some understanding and to generate a draft idea, then I openly contacted people to find out who could help me on the project. It took several months, but it eventually led to a successful collaborative project and our first paper was just recently published. We experienced a “culture shock” while drafting the paper. The research team members without a mathematics/machine learning background drafted the paper in Word, but the mathematics team drafted the paper in Latex! Imagine when we sent each other drafts to review and comment. The maths people said that in their field if people don’t know how to write in latex, they are not true researchers!
Do you have any interesting resources or helpful networks people should know about?
Yes. For people who are new to digital health, stay connected in this digital health and informatics network and read newsletters like this one. Sydney Informatics Hub is very helpful. They offer regular courses on programming, RedCap, Artermis computation and more. They also provide one to one consultation.
LinkedIn Learning (Lynda.com) is another good source to learn machine learning and is free for University of Sydney staff.
IEEE world congress on computational Intelligence is a good conference to connect people who are interested in digital health. This year, the meeting will be run virtually with reduced registration fees. https://wcci2020.org/
You can connect with Jingjing on Twitter @youjingjing
A huge thank you to Jingjing You for being our May 2020 feature!
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