Big Data Chalk & Talk/Brown Bag: Gari Clifford

Add to Calendar
Date:
February 27, 2014 12:30 pm - 2:00 pm
Location:
MiRC 102 A&B

Gari Clifford
Associate Professor of Bioinformatics and Biomedical Engineering
Emory and Georgia Tech

Four Georgia Tech research hubs have launched a new “chalk & talk” brown bag lunch series on Big Data. The weekly series, sponsored jointly by the Institute for Data & High Performance Computing (IDH), Institute for Materials (IMaT)Center for Data Analytics (CDA) and Center for High Performance Computing (HPC) will be held on most Thursdays during the Fall and Spring Semesters and feature a mix of topics, including those related to big data for materials and manufacturing, as well as other topics critical to the broader area of big data.

All meetings are held on Thursdays during lunchtime. 

Date: February 27
Topic: “Time Series Data in Healthcare: From Mental Health to the ICU”
Presenter: Gari Clifford

Abstract:

The enormous volume of available data from patients in both the hospital and outpatient settings presents both exciting opportunities for healthcare and several key problems. These include:

  1. Traditional time series analysis algorithms are tuned to be over-sensitive, which results in many false alarms, and the expectation that an expert will over-read each alarm. With patient-to-doctor ratios ranging from 100 to 50,000 to one, this paradigm will no longer address the issues.
  2. Multiple sensors can record similar information, and so we must build trust metrics to identify the signals we can trust, or work out ways to combine the signals together in a robust manner.
  3. Humans disagree on diagnoses and labels, even when the disease is well described. Inter- and intra-human bias and variance in diagnoses must be addressed, particularly if we are to build automated algorithms from the labeled data.
  4. Data labeling of medical data is vast and most likely impossible to do so by hand. Low-cost, semi-supervised and unsupervised approaches to event labeling, state definition, and feature extraction are needed. Finding clinically acceptable approaches to address this problem may be key to developing appropriate predictive machine learning algorithms.

Bio:

Gari Clifford recently joined the Emory and Georgia Tech faculties as an associate professor of bioinformatics and biomedical engineering. Previously, he was an associate professor of biomedical engineering at Oxford University and the director of the Centre for Doctoral Training in Healthcare Innovation at Oxford’s Institute of Biomedical Engineering.

Clifford’s research group explores machine learning and signal processing to extract actionable information from medical data. In particular, the lab focuses on intensive care medicine, cardiovascular disease, circadian rhythm disorders, sleep, and mental health. This research is aligned with the concept of sustainable healthcare, and he has a particular interest in mHealth in resource-constrained settings and circadian rhythms.

Prior to joining the faculty at Oxford, Clifford was a principal research scientist at MIT, where he spent six years managing the engineering effort behind a multi-million dollar project to collect and analyze the world's largest public database of hospital data. Clifford also serves on the international advisory and editorial boards of several organizations, including the NIH Public Access Resource, PhysioNet, and the Institute of Physics' Journal of Physiological Measurement.

In addition to licensing several patents, Clifford has been closely involved in the regulatory approval of medical devices for more than ten years. Also, he has received several awards for his research, including the 2009 Martin Black Prize, the 2010 mHealth Alliance Award, the 2011 International Engineering World Health Design Competition, the Dell Best Innovation Leveraging Technology Award in 2012, and the Computing in Cardiology Challenges in 2008, 2012, and 2013 for ECG analysis and mortality prediction.