To join via Zoom: To join this seminar, please request Zoom connection details from headsec [at] stat.ubc.ca.
Time: 4:00pm – 4:30pm
Speaker: Jeffrey Yiu, UBC Statistics MSc student
Title: Comparing public and private long-term care outcomes in British Columbia
Abstract: In British Columbia, approximately 90% of long-term care beds for seniors are publicly subsidized, while the remaining beds are privately paid. A 2018 report from the Office of the Seniors Advocate, an independent office of the B.C. provincial government, found that there were differences in emergency department visits, hospitalization rates, and mortality, depending on whether beds were publicly subsidized or privately paid. During my co-op terms with the B.C. Ministry of Health, I explored health databases such as the Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) to compare health outcomes between public and private long-term care clients in the 2019/20 fiscal year. Areas of investigation included demographics, morbidity, mortality, and utilization of health services.
Time: 4:30pm – 5:00pm
Speaker: Pramoda Jayasinghe, UBC Statistics MSc student
Title: Working with health data and consulting for health sector clients
Abstract: Health data helps inform many decisions ranging from public health policies to showing the efficacy of a new drug. Due to the sensitivity of health data and the expectations of clients using said data, consulting for health-related projects has a unique set of challenges. While working on multiple projects during my co-op at Broadstreet, an organization that provides consulting services for health economics and outcomes research, I got to experience some of the practical issues that exist when consulting for health sector clients, specifically. The main objective of this presentation is to point out some aspects to consider when working with real-world health data.
Time: 5:00pm – 5:30pm
Speaker: Li Zha, UBC Statistics MSc student
Title: Aspects of working with data between industry and health research
Abstract: Machine learning has been widely adopted in many applications in industry and research in recent years. In this presentation, I will first introduce my industrial experience as a data science intern with TD Personal Banking where I mainly focused on building a propensity score matching for part of their test tool, and exploring data pruning to deal with noisy texts for fraud detection. Afterwards, I switched to the health industry by working on a polygenic risk score using GWAS (Genome-wide genetic association studies) data under Prof. Park’s lab in BC Cancer. This presentation will focus more on sharing my learnings with working in different industries and provide some insights to those who are interested in data science roles.