Fwd: FW: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - JANUARY 26, 2022 - 3PM (EST) ---ZOOM INFO BELOW

Fri Jan 21 16:12:14 EST 2022

This can be of interest to many of us.

Cheers,
Artur

---------- Forwarded message ---------
From: Christy Melucci <cmelucci at andrew.cmu.edu>
Date: Fri, Jan 21, 2022 at 4:01 PM
Subject: FW: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - JANUARY 26,
2022 - 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, January 21, 2022 3:46 PM
*Cc:* Batmanghelich, Kayhan <kayhan at pitt.edu>; Visweswaran, Shyam <
shv3 at pitt.edu>
*Subject:* MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - JANUARY 26,
2022 - 3PM (EST) ---ZOOM INFO BELOW

*Machine Learning in Medicine (MLxMed)*

*A Virtual Seminar Series in Pittsburgh*

*Hosted by the Department of Biomedical Informatics*

*Wednesday, January 26, 2022*

*3:00 PM – 4:00 PM Eastern Time University of Pittsburgh, UPMC, and CMU*

*Artificial Intelligence in Clinical Medicine:*

*What makes a good machine learning model for clinical applications?*

*Zoom* *https://pitt.zoom.us/j/97941360439*
<https://pitt.zoom.us/j/97941360439>

*(**details are listed at the end**)*

*Collin M. Stultz, MD, PhD*

Nina T. and Robert H. Rubin Professor in Medical Engineering and Science,

Professor of Electrical Engineering and Computer Science, Massachusetts
Institute of Technology

*Abstract: *Although applications of Machine Learning (ML) are now
pervasive in the clinical literature, ML has yet to be embraced by the
clinical community.  So, what constitutes a good machine learning model for
clinical applications?   Certainly, a necessary condition for the success
of any machine learning model is that it achieves an accuracy that is
superior to pre-existing methods.  In the healthcare sphere, however,
accuracy alone does not, nor should it, ensure that a model will gain
clinical acceptance.  In view of the fact that no model, in practice, has
100% accuracy, attempts to understand when a given model is likely to fail
should form an important part of the evaluation of any machine learning
model that will be used clinically.  Moreover, the most useful clinical
models are explainable in the sense that it is possible to clearly
articulate why the model arrives at a particular result for a given set of
inputs. In this talk I will expand upon these challenges that make the
creation of clinically useful ML models particularly difficult, and discuss
ways in which they can be overcome.

*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: January 26, 2022 03:00 PM Eastern Time (US and Canada)*

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

*https://pitt.zoom.us/j/97941360439* <https://pitt.zoom.us/j/97941360439>

Or One tap mobile :

US*: +12678310333,,97941360439#  or 8778535247,,97941360439#* (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: 979 4136 0439*

International numbers available*: **https://pitt.zoom.us/u/ahon5yBXB*
<https://pitt.zoom.us/u/ahon5yBXB>

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 Sydney)

103.122.167.55 (Australia Melbourne)

149.137.40.110 (Singapore)

64.211.144.160 (Brazil)

149.137.68.253 (Mexico)

69.174.57.160 (Canada Toronto)

65.39.152.160 (Canada Vancouver)

207.226.132.110 (Japan Tokyo)

149.137.24.110 (Japan Osaka)

*Meeting ID: 979 4136 0439*

*SIP: **97941360439 at zoomcrc.com* <97941360439 at zoomcrc.com>

*Genine M. Bartolotta*

*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)*

bartolottagm 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).*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/autonlab-users/attachments/20220121/3eb9ae6a/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - January 26, 2022 - 3 PM (EST).pdf
Type: application/pdf
Size: 168999 bytes
Desc: not available
URL: <http://mailman.srv.cs.cmu.edu/pipermail/autonlab-users/attachments/20220121/3eb9ae6a/attachment-0001.pdf>