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Upcoming Talks

 Samantha Ravich

 

 

Samantha Ravich
Deputy Chair of the President's Intelligence Advisory Board
Cyber-Enabled Economic Warfare: Why America‚Äôs Private Sector is now on
the Front Lines of an Emerging Battlefield
Thursday, September 27, 2018
4:30pm-6:00pm
Haldeman 41 (Kreindler Conference
Room)

Past Talks

William Regli, Ph.D

William Regli, Ph.D.
Director of the Institute for Systems Research at the Clark School of Engineering, 
Professor of Computer Science at the 
University of Maryland at College Park
A New Type of Thinking
Friday, June 22, 2018
Life Sciences Center 105
11:00 AM

Tata Consulting Logo

Dr. Gautam Shroff
Vice President, Chief Scientist, and Head of Research at Tata Consultancy Services 
Enterprise AI for Business 4.0: from Automation to Amplification
Thursday, June 07, 2018
Haldeman 041 Kreindler Conference Room
3:30 PM

John Dickerson UMD

John P Dickerson
Assistant Professor, Department of Computer Science, University of Maryland
Using Optimization to Balance Fairness and Efficiency in Kidney Exchange
Monday,  May 21st
Kemeny Hall 008
3:30 PM

Senator Jeanne Shaheen

Jeanne Shaheen
U.S. Senator from New Hampshire
Russian Interference in American Politics and Cyber Threats to Our Democracy
Tuesday, February 20, 2018
Alumni Hall (Hopkins Center)
11:00 AM

Lisa Monaco

Lisa Monaco
Former Homeland Security Advisor to President Obama
In Conversation: Lisa Monaco, Fmr Homeland Security Advisor to President Obama
Tuesday, February 13, 2018
Filene Auditorium (Moore Building)
5:00 PM
Sponsored by The Dickey Center for International Understanding

John Stewart EPRI

John Stewart
Sr. Technical Leader, Cyber Security, EPRI
Securing Grid Control Systems
Friday, January 12, 2018
Sudikoff L045 Trust Lab
12:00 Noon

M. Todd Henderson

M. Todd Henderson
Professor of Law, University of Chicago
Hacking Trust: How the Social Technology of Cooperation Will Revolutionize Government
Thursday, January 11, 2018
5:00pm-6:30pm 
Room 003, Rockefeller Center
Sponsored by: Rockefeller Center

Dr. Liz Bowman

Dr. Elizabeth Bowman
U.S. Army Research Laboratory
Artificial Intelligence, Machine Learning and Information: Army Social Computing Research
Tuesday, December 5th
Haldeman 041 Kreindler Conference Room
4:00 PM

Dr. Fabio Pierazzi

Dr. Fabio Pierazzi
Royal Holloway University of London
Network Security Analytics for Detection of Advanced Cyberattacks
Tuesday, November 28th
Sudikoff Trust Lab (L045)
12:30 PM

V.S. Subrahmanian

V.S. Subrahmanian
Dartmouth Distinguished Professor in Cybersecurity, Technology, and Society
Bots, Socks, and Vandals
Tuesday, November 14th
Carson L01
5:00 PM 

Rand Beers

Rand Beers ('64)
Big Data, the Internet, and Social Media:  The Road to the November 2016 Election
Wednesday, November 8th
Haldeman 41 (Kreindler Conference Hall)
4:30 PM 

Fright Night Imge

Wanna See Something REALLY Scary?
ISTS Looks at the Dark Web on Halloween Night
Tuesday, October 31st
S
udikoff  045 Trust Lab (dungeon)
7:30 PM - RSVP
Space is Limited 

Sal Stolfo

Salvatore J. Stolfo 
Columbia University
A Brief History of Symbiote Defense
Tuesday, October 31st
Rockefeller 003
5:00 PM

ISTS Information Pamphlet


2012BrochureCover

 

Institute for Security, Technology, and Society
Dartmouth College
6211 Sudikoff Laboratory
Hanover, NH 03755 USA
info.ists@dartmouth.edu
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Leveraging Data Across Time and Space to Build Predictive Models for Healthcare-Associated Infections

Friday, October 31, 2014 at 1:45pm
Steele 006
Jenna Wiens
Assistant Professor, EECS Department, The University of Michigan
Co-sponsored by the Trustworthy Health and Wellness project (thaw.org) and ISTS

Abstract

Jenna Weins
Jenna Weins

The proliferation of electronic medical records holds out the promise of using machine learning and data mining to build models that will help healthcare providers improve patient outcomes. However, building useful models from these datasets presents many technical problems. The task is made challenging by the large number of factors, both intrinsic and extrinsic, influencing a patient's risk of an adverse outcome, the inherent evolution of that risk over time, and the relative rarity of adverse outcomes.

In this talk, I will describe the development and validation of hospital-specific models for predicting healthcare-associated infections (HAIs), one of the top-ten contributors to death in the US. I will show how by adapting techniques from time-series classification, transfer learning and multi-task learning one can learn a more accurate model for patient risk stratification for the HAI Clostridium difficile (C. diff).

Applied to a held-out validation set of 25,000 patient admissions, our model achieved an area under the receiver operating characteristic curve of 0.81 (95%CI 0.78-0.84). On average, we can identify high-risk patients five days in advance of a positive test result. Clinicians at the hospital are now considering ways in which that information can be used to reduce the incidence of HAIs.

Bio

I am an Assistant Professor in EECS at the University of Michigan. In the fall of 2014, I joined the CSE division after completing my PhD at MIT.

My primary research interests lie at the intersection of machine learning and medicine. I especially enjoy solving the technical challenges that arise when considering the practical application of machine learning in clinical settings. Currently, I'm focused on developing accurate patient risk stratification approaches that leverage data across time and space, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US.

In addition to this work, I've had the privilege of working with a unique dataset from the NBA. Recently, I've had a lot of fun applying many of the same techniques we use in the medical work to the world of sports analytics. In general, I enjoy tackling the challenges that develop when working with large complex datasets.

Last Updated: 10/13/14