=Paper=
{{Paper
|id=Vol-2029/ks3
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2029/ks3.pdf
|volume=Vol-2029
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==None==
https://ceur-ws.org/Vol-2029/ks3.pdf
Learning to Cure
Regina Barzilay
Massachusetts Institute of Technology
32 Vassar Street, 32-G468, Cambridge, MA 02139, USA
regina@csail.mit.edu
Abstract provide rationales underlying their predic-
tions, and semi-supervised methods for in-
Cancer inflicts a heavy toll on our society. formation extraction.
One out of seven women will be diagnosed
with breast cancer during their lifetime,
a fraction of them contributing to about
450,000 deaths annually worldwide. De-
spite billions of dollars invested in cancer
research, our understanding of the disease,
treatment, and prevention is still limited.
Majority of cancer research today takes
place in biology and medicine. Computer
science plays a minor supporting role in
this process if at all. In this talk, I hope
to convince you that NLP as a field has
a chance to play a significant role in this
battle. Indeed, free-form text remains the
primary means by which physicians record
their observations and clinical findings.
Unfortunately, this rich source of textual
information is severely underutilized by
predictive models in oncology. Current
models rely primarily only on structured
data.
In the first part of my talk, I will de-
scribe a number of tasks where NLP-based
models can make a difference in clini-
cal practice. For example, these include
improving models of disease progression,
preventing over-treatment, and narrowing
down to the cure. This part of the talk
draws on active collaborations with oncol-
ogists from Massachusetts General Hospi-
tal (MGH).
In the second part of the talk, I will push
beyond standard tools, introducing new
functionalities and avoiding annotation-
hungry training paradigms ill-suited for
clinical practice. In particular, I will fo-
cus on interpretable neural models that
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