Fwd: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - APRIL 14, 2021 - 3PM (EST) ---ZOOM

[Artur Dubrawski]

some of us may be interested in this topic

---------- Forwarded message ---------
From: Christy Melucci <cmelucci at cs.cmu.edu>
Date: Wed, Apr 7, 2021 at 4:12 PM
Subject: RE: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - APRIL 14,
2021 - 3PM (EST) ---ZOOM
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:* Wednesday, April 7, 2021 3:22 PM
*Cc:* Batmanghelich, Kayhan <kayhan at pitt.edu>; Visweswaran, Shyam <
shv3 at pitt.edu>
*Subject:* MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - APRIL 14, 2021
- 3PM (EST) ---ZOOM



*Machine Learning in Medicine (MLxMed)*

*A Virtual Seminar Series in Pittsburgh*

*Hosted by the Department of Biomedical Informatics*



*Wednesday, April 14, 2021*


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



*Machine Learning in Medicine: Early Recognition of Sepsis*



*Zoom **https://pitt.zoom.us/j/93487765055*
<https://pitt.zoom.us/j/93487765055>

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



*Karsten Borgwardt, PhD*

Full Professor of Data Mining, Biosystems, ETH Zürich



*Abstract: *Sepsis is a major cause of mortality in intensive care units
around the world. If recognized early, it can often be treated
successfully, but early prediction of sepsis is an extremely difficult task
in clinical practice. The data wealth from intensive care units that is
increasingly becoming available for research now allows to study this
problem of predicting sepsis using machine learning and data mining
approaches. In this talk, I will describe our efforts towards data-driven
early recognition of sepsis.





*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: April 14, 2021 - 3:00 PM Eastern Time (US and Canada)*



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

*https://pitt.zoom.us/j/93487765055* <https://pitt.zoom.us/j/93487765055>



Or One tap mobile :

    US: *+12678310333, 93487765055#  or 8778535247, 93487765055#* (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: 934 8776 5055*



    International numbers available: *https://pitt.zoom.us/u/abbaYni0lZ*
<https://pitt.zoom.us/u/abbaYni0lZ>



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)

    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: 934 8776 5055*

*    SIP: **93487765055 at zoomcrc.com* <93487765055 at zoomcrc.com>
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