van der Schaar Lab

Inspiration Exchange: time series in healthcare

The van der Schaar Lab’s eleventh Inspiration Exchange engagement session took place virtually on October 7, 2021, and was attended by students and professionals from the AI and machine learning community.

The focus of the session was time series—an absolutely vital topic (and key research pillar for the van der Schaar Lab) that touches all aspects of machine learning for healthcare. The first half of the session featured short presentations given by Mihaela van der Schaar, former Ph.D. student Changhee Lee, and research engineer Evgeny Saveliev.

The latter half of the session featured a Q&A, in which Mihaela and members of the lab answered questions from audience members about time series in healthcare.

Introduction – 0:00
Announcement regarding Ph.D. student recruitment – 3:16
Presentation by Mihaela van der Schaar [time series: challenges & solutions ] – 5:11
Presentation by Changhee Lee [temporal phenotyping] – 18:45
Presentation by Evgeny Saveliev [time series ML toolkit demonstration] – 27:44
Q&A question 1 – 40:58
Q&A question 2 – 45:24
Q&A question 3 – 50:10
Q&A question 4 – 53:13
Q&A question 5 – 56:30
Q&A question 6 – 1:00:06
Q&A question 7 – 1:03:42
Intro to next sessions – 1:08:09

For more info related to time series in healthcare, please view our overview here.

A full list of the lab’s publications can be found here.

Nick Maxfield

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.