Exploring the benefits of Artificial Intelligence in Healthcare.

AI Health Lab | UT - iSchool

An academic lab focusing on cutting-edge technologies with data science

SuitClub is led by Prof. Ying Ding from School of information, and Prof. Justin Rousseau from Dell Medical School at the University of Texas at Austin. SuitClub is made up of scholars and students from different fields and disciplines. We focus on cutting-edge research on AI in health and data-driven science of science. Our research is concentrated but not limited to the following topics:

AI in Health

● Building software to power evidence-based care by using clinical practices and patient data.
● Building a large scale knowledge graph for PubMed.
● Building a bioportal to connect researchers with bio entities and develop AI algorithms.

AI in Medicine

● Building a large-scale knowledge graph by integrating 20+ datasets in drug discovery.
● Developing deep graph mining algorithms for drug discovery.

Data-Driven Science

● Developing entity metrics by applying bibliometric methods to medicine.
● Measuring the pace of AI innovations from the perspectives of innovation diffusion, team collaboration, labor division, and diversity.
● Selfish knowledge: understanding scientific success and self-promotions.

AI Health Data Challenge

A competition for all scholars and students who are interested in promoting data-driven and AI-driven approaches to enable better health.

Research

An end-to-end semi-supervised cross-modal contrastive learning framework, that simultaneously performs disease classification and localization tasks...

Introducing two unique positive sampling strategies specifically tailored for EHR data...

Knowledge graph analytics platform with LINCS and IDG for Parkinson’s disease target illumination

we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases...

Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop

An end-to-end semi-supervised cross-modal contrastive learning framework, that simultaneously performs disease classification and localization tasks.

Events