Fwd: Seminar: David Page, Monday Faculty Research Seminars

[Artur Dubrawski]

this Heinz seminar's topic may be very relevant to many of us interested in
healthcare AI

Cheers
Artur


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From: <aosial at andrew.cmu.edu>
Date: Fri, Oct 7, 2022 at 10:19 AM
Subject: Seminar: David Page, Monday Faculty Research Seminars
To: <heinz-faculty at lists.andrew.cmu.edu>, <heinz-phd at lists.andrew.cmu.edu>,
<chuqingj at andrew.cmu.edu>, <salomonsanna at gmail.com>, <aosial at andrew.cmu.edu>


Seminar: David Page, Monday Faculty Research Seminars



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Seminar: David Page, Monday Faculty Research Seminars
Monday Oct 17, 2022 ⋅ 12pm – 1:20pm (Eastern Time - New York)
Please join us on *Monday, October 17th*, from *12:00 PM – 01:20 PM*
in *Hamburg
Hall, Room 1002* as part of the Monday Faculty Research Seminars (MFRS).

The *schedule is now open* to reserve your time to meet with David
Page on *October
17th*: *Seminar Scheduler*
<https://www.google.com/url?q=https%3A%2F%2Fseminartracker.tepper.cmu.edu%2FPrivateSeminarSchedule%3FSeminarId%3D113&sa=D&ust=1665584280000000&usg=AOvVaw2Tuu-wSkeLOy5pptqvX-ZL>

*Presenter: *David Page is a Professor of Biostatistics & Bioinformatics at
Duke University. He works on algorithms for data mining and machine
learning, and their applications to biomedical data, especially
de-identified electronic health records and high-throughput genetic and
other molecular data. Of particular interest are machine learning methods
for complex multi-relational data (such as electronic health records or
molecules as shown) and irregular temporal data, and methods that find
causal relationships or produce human-interpretable output (such as the
rules for molecular bioactivity shown in green to the side).

*Paper:*  "Prediction and Causation in EHRs"

*Abstract: *This talk begins with two empirical results in prediction in
electronic health records (EHRs). First occurrence of thousands of ICD
codes can be predicted with average AUC above 0.7, and accuracies can be
further significantly improved by using family histories constructed
entirely automatically from de-identified patient data.  The talk then
turns to tasks where causal discovery is needed rather than merely
prediction.  Motivated first by empirical results with neural point
processes and other methods, we propose a theoretical model of causal
discovery that adds time and sample complexity to the normal formulations.
This model shows that some traditional machine learning algorithms can be
used for causal discovery, if they are modified to consider unobserved or
partially-observed confounders, including time-varying confounders.



______________________________________________________
See the *Heinz Event Calendar*
<https://calendar.google.com/calendar/embed?src=heinz-events%40andrew.cmu.edu&ctz=America%2FNew_York>
for
additional upcoming Seminars
LocationLocation: HBH 1002 (In Person)
View map
<https://www.google.com/maps/search/Location:+HBH+1002+%28In+Person%29?hl=en>
Organizer
heinz-events at andrew.cmu.edu
Guests

--

George H. Chen
Assistant Professor, Heinz College of Information Systems and Public Policy
Affiliated Faculty, Machine Learning Department
Carnegie Mellon University
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