Data-Driven Approaches for Effective and Timely Healthcare Service

Date and time: 
Thu, Feb 24 2022 - 12:00pm to 1:00pm
Lu Wang
University of Toronto

Thien Nguyen <>

Healthcare systems are changing in the era of big data. Advances of artificial intelligence (AI) in healthcare make it possible for healthcare providers to sift through tremendous amounts of information efficiently, which eventually help them take care of their patients better. There are various types of health information ranging from medical literature to pathology reports. How to develop and apply Machine Learning (ML) methods that can efficiently utilize Electronic Health/Medical Records (EHRs/EMRs) is significant to facilitate decision making of physicians in their clinical practice.
Albeit the last few years have witnessed an explosive increase of healthcare data in terms of volume, variety and veracity, it is insufficient to build a robust prediction model in various scenarios due to time, geographical and domain inherent constraints. Due to the aforementioned constraints, EHRs data often emerges with unbalanced groups of instances and censored instances, e.g., survival data. The integration of Multi-task learning (MTL) and Survival Analysis (SA) provides a paradigm to alleviate survival data insufficiency by bridging survival data from all SA tasks and improves generalization performance of all SA tasks involved.
In addition to ML methods, inspired by the motivation of human-in-the-loop, Human-Centered AI (HCAI) for Data Driven Decision Making (D3M) addressing healthcare/medicine problems attracts more attention to improve the situation awareness and the quality of decisions. More specifically, interactive Machine Learning (iML) improves the ML prediction by looping human experts in the learning process and integrating human expertise. From another perspective, eXplainable Artificial Intelligence (XAI) and trustworthy AI in healthcare systems not only improve the uptake of ML model but also increase physician trust in ML prediction for clinical decision making.


Lu Wang is currently a Ph.D. candidate in Industrial Engineering at the University of Toronto working as the data science team lead and a graduate research assistant directed by Prof. Mark Chignell, and she expects to graduate in Summer 2022. Before that she received her Ph.D. degree in Computer Science from Wayne State University in 2019. Her primary research interests are developing and applying Machine Learning (ML), Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records (EHRs/EMRs), and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer's disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence (HCAI) tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support. She has published over 20 journal and conference papers including 15 first authored ones in the top venues including proceedings of IEEE International Conference on Data Mining (IEEE ICDM), Data Mining and Knowledge Discovery (DMKD Journal), ACM Transactions on Computer-Human Interaction (TOCHI), American Medical Informatics Association (AMIA) Informatics Summit, IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), IEEE EMBS International Conference on Biomedical and Health Informatics (IEEE EMBS BHI), IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), Alzheimer's Association International Conference (AAIC), International Symposium on Bioinformatics Research and Applications (ISBRA), IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE), Neurocomputing journal, etc. One of her first authored papers, which was published on IEEE ICMLA, was a best paper top 3 finalist in 2017.