Search for tag: "informatics"

This talk will discuss the principles of epidemiologic thinking and a foundation for population health sciences. Basic epidemiologic concepts including how best to answer a scientific question,…

Sparse data computations are ubiquitous in science and engineering. Two widely used applications requiring sparse data computations are graph algorithms and linear algebra operations such as Sparse…

The massive generation and collection of digitized health data, e.g. electronic health records (EHRs), along with these notable, longstanding healthcare deficiencies including diagnostic errors and…

By driving the digitization of healthcare data, the 2009 HITECH Act set the stage for business and business practice transformation. Today, the amount of healthcare data doubles every two years and…

Many medicines become toxic only after bioactivation by metabolizing enzymes. Often, metabolic enzymes transformed them into chemically reactive species, which subsequently conjugate to proteins and…

The explosion of health-related data in the form of electronic health records and genomics has captured the attention of both the machine learning and medical informatics communities. In his talk,…

Much of the world’s real data on people is irregularly-sampled, temporal, and observational (meaning we don’t get to experiment as in a randomized clinical trial). For example,…

How can we distill multiple aspects of raw and noisy electronic health record data, such as diagnoses, medications and procedures, to a few concise clinical states without human-annotated labels? In…

Conducting health research is complex, from accessing health data to running analytics to building interoperable applications. The Georgia Tech Health Data Analytics Platform (HDAP) is designed to…

More than half of kidney transplant recipients are rehospitalized in the year after kidney transplantation, placing a large burden on the hospital system and placing patients at higher risk for poor…