[Dmbu-l] Can Machine Learning Help us Improve Physician Communication? Friday @ 11AM, MCS 148

Charalampos Mavroforakis cmav at bu.edu
Wed Nov 6 16:31:22 EST 2013

Hi all,

On Friday we will be having prof. Byron Wallace from Brown University.
Details about his talk follow:

*Title: Can Machine Learning Help us Improve Physician Communication?*
Speaker: Byron Wallace, Research Assistant Professor, Brown University
 *Date:* Friday, November 8, 2013 at 11am in MCS 148

Physician-patient communication is a critical component of health-care.
Several studies have reported an association between metrics of
physician-patient communication quality and health outcomes, and there is
evidence that the relationship between the physician and the patient
affects patient satisfaction and burden of symptoms. Recognizing its
importance, health sciences researchers have investigated clinical
communication at length, but this work has been predominantly qualitative
in nature. There is a pressing need to better understand clinical
interaction processes quantitatively. To this end, researchers have
recently introduced coding schemas that annotate the utterances in
transcribed physician-patient interactions with codes that capture
clinically meaningful properties of speech. Modern schemas are
multidimensional: they capture both subject matter (discussion topics) and
interaction processes (speech acts). Analyzing transcripts coded with such
schemas (coupled with health outcomes data) may reveal important,
reproducible properties of physician communication that correlate with
outcomes of interest.

But two major limitations preclude progress on this front: (1) fine-grained
coding of transcripts is laborious and expensive, limiting sample sizes and
hence the set of questions that can be addressed; and (2) we lack models
suitable to the multi-dimensional, sequential nature of physician-patient
interactions. Machine learning approaches can potentially help solve both
problems. In this talk I will present emerging work toward this end.
Specifically, I will present a generative model of clinical communication
that jointly captures both the topics and the speech acts used in each
utterance. I will also present some preliminary results leveraging output
from this model, and I will conclude by discussing future directions for
this work.

Byron Wallace is an assistant professor (research) in the Department of
Health Services, Policy & Practice at Brown University; he is also
affiliated with the Brown Laboratory for Linguistic Processing (BLLIP) in
the department of Computer Science. His research is in data mining/machine
learning and natural language processing with an emphasis on applications
in health informatics. Before moving to Brown, he completed his PhD in
computer science under the supervision of Carla Brodley. He was awarded the
Tufts Outstanding Graduate Researcher at the Doctoral Level award in 2012
and was selected as the runner-up for the 2013 ACM SIGKDD Doctoral
Dissertation Award for his thesis work, which concerned developing novel
machine learning methods to make conducting biomedical systematic reviews
more efficient.

- Harry
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