Fwd: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - OCTOBER 28, 2020 - 3PM (EST) --- ZOOM INFO BELOW

Fri Oct 23 16:45:38 EDT 2020

may be of interest to some of us working in HC applications of ML

Artur

---------- Forwarded message ---------
From: Christy Melucci <cmelucci at cs.cmu.edu>
Date: Fri, Oct 23, 2020 at 12:31 PM
Subject: RE: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - OCTOBER 28,
2020 - 3PM (EST) --- ZOOM INFO BELOW
To: ml-core-faculty at cs.cmu.edu <ml-core-faculty at cs.cmu.edu>,
ml-seminar at cs.cmu.edu <ml-seminar at cs.cmu.edu>
Cc: Visweswaran, Shyam <shv3 at pitt.edu>, Bartolotta, Genine M <
bartgm at pitt.edu>, Batmanghelich, Kayhan <kayhan at pitt.edu>, Roni Rosenfeld <
roni at cs.cmu.edu>

*From:* Bartolotta, Genine M <bartgm at pitt.edu>
*Sent:* Friday, October 23, 2020 11:08 AM
*Cc:* Batmanghelich, Kayhan <kayhan at pitt.edu>; Visweswaran, Shyam <
shv3 at pitt.edu>
*Subject:* MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - OCTOBER 28,
2020 - 3PM (EST) --- ZOOM INFO BELOW
*Importance:* High

Machine Learning in Medicine (MLxMed) A Virtual Seminar Series in Pittsburgh

*Hosted by the Department of Biomedical Informatics*

*  Wednesday, October 28, 2020 3:00 PM – 4:00 PM Eastern Time*

*University of Pittsburgh, UPMC, and CMU*

Going Beyond Diagnosis and Prognosis: Machine Learning to Guide Treatment
Suggestions

*Zoom https://pitt.zoom.us/j/94305382303
<https://pitt.zoom.us/j/94305382303>*

(*details are listed at the end*)

David Sontag, PhD

Associate Professor, Department of Electrical Engineering and Computer
Science (EECS)

Massachusetts Institute of Technology

*Abstract: *The next decade will see a shift in focus of machine learning
in healthcare from models for diagnosis and prognosis to models that
directly guide treatment decisions. We introduce methods for learning
treatment policies from electronic medical records and demonstrate their
use in learning to recommend antibiotics for women with uncomplicated
urinary tract infections. Our methods can take into consideration multiple
factors, e.g. efficacy, cost, risk of complications, that should be
optimized when learning policies. We show how to perform policy
distillation, after learning, to simplify deployments. We introduce the
concept of a 'target deployment' to guide retrospective evaluation, showing
how this can be used to obtain fair comparisons to existing clinical
practice. We find that, relative to clinicians, our models reduce
inappropriate antibiotic prescriptions from 11.9% to 9.5% while at the same
time using 50% fewer second-line antibiotics. Finally, we discuss mistakes
that we made and lessons learned. Based on joint work with Sooraj
Boominathan, Michael Oberst, Helen Zhou, and Sanjat Kanjilal (BWH/MGH).

*About MLxMed Seminar Series*

(*http://ml-in-medicine.org/*
<https://nam05.safelinks.protection.outlook.com/?url=http%3A%2F%2Fml-in-medicine.org%2F&data=02%7C01%7Ccafeo%40pitt.edu%7Cfd9284768d884c01352b08d816ea4fa8%7C9ef9f489e0a04eeb87cc3a526112fd0d%7C1%7C0%7C637284542905912546&sdata=6NKlakpjZ8taWQlU7RklCdS%2F7tDHw6SIhKAfiZYtr8M%3D&reserved=0>
)

Medicine is complex and data-driven while discovery and decision making are
increasingly enabled by machine learning. Machine learning has the potential
to support, enable and improve medical discovery and clinical decision
making in areas such as medical imaging, cancer diagnostics, precision
medicine, clinical trials, and electronic health records. This seminar
series focuses on new algorithms, real-world deployment, and future trends
in machine learning in medicine. It will feature prominent investigators who
are developing and applying machine learning to biomedical discovery
and in clinical
decision support. For more information see MLxMed website.

Zoom Information

*When: October 28, 2020 - 3:00 PM Eastern Time (US and Canada)*

*Please click the link below to join the webinar:*

*https://pitt.zoom.us/j/94305382303 <https://pitt.zoom.us/j/94305382303>*

*Or iPhone one-tap:*

US: +12678310333, 94305382303# or 8778535247, 94305382303# (Toll Free)

*Or Telephone:*

Dial (for higher quality, dial a number based on your current location):
US: +1 267 831 0333 or 877 853 5247 (Toll Free)

*Webinar ID: 943 0538 2303*

International numbers available: * https://pitt.zoom.us/u/abuJpMGV4A
<https://pitt.zoom.us/u/abuJpMGV4A>*

Or an H.323/SIP room system:

H.323: 162.255.37.11 (US West)

162.255.36.11 (US East)

115.114.131.7 (India Mumbai)

115.114.115.7 (India Hyderabad)

213.19.144.110 (Amsterdam Netherlands)

213.244.140.110 (Germany)

103.122.166.55 (Australia)

149.137.40.110 (Singapore)

64.211.144.160 (Brazil)

69.174.57.160 (Canada)

207.226.132.110 (Japan)

*Meeting ID: 943 0538 2303*

SIP: *94305382303 at zoomcrc.com <94305382303 at zoomcrc.com>*

*Genine M. Bartolotta*

*Department Purchasing,*

*Service & Supplier Agreements,*

*License & License Renewals*

*NMVB Administrative & Event Coordinator*

*Administator for:*

*IT Support*

*Uma Chandran, PhD, MSIS*

*John R. Gilbertson, MD*

*Songjian Lu, PhD*

*Department of Biomedical Informatics*

*University of Pittsburgh, School of Medicine *

The Offices at Baum, Fourth Floor

5607 Baum Boulevard

Pittsburgh, PA  15206-3701

Phone: (412) 624-5100

Cell Phone: (412) 877-4872

FAX: (412) 648-9118

E-mail: *bartgm at pitt.edu <bartgm at pitt.edu> (preferred)*

             bartgm at upmc.edu

*This e-mail may contain confidential information of the sending *

*organization. Any unauthorized or improper disclosure, copying, *

*distribution, or use of the contents of this e-mail and attached *

*document(s) is prohibited.  The information contained in this *

*e-mail and attached document(s)is intended only for the *

*personal and confidential use of the recipient(s) named *

*in original e-mail and attached document(s).*
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