NIPS 2013 Workshop on
Machine Learning for Clinical Data Analysis and Healthcare
=== Call for Submissions ===
When: A one day (Dec. 9th or 10th) Workshop at NIPS
Where: Lake Tahoe, Nevada, USA
Website: Machine Learning for Clinical Data Analysis & Healthcare
Description:
Advances in medical information technology have resulted in enormous
warehouses of data that are at once overwhelming and sparse. A single
patient visit may result in thousands of measurements, and hospitals may
see thousands of patients each year. However, the average patient has
relatively few visits to any particular medical provider. The resulting
data are a heterogeneous amalgam of patient demographics, vital signs,
diagnoses, records of treatment, medical images, lab tests, and
annotations made by nurses or doctors, each with its own idiosyncrasies.
The objective of this workshop is to discuss how advanced machine learning
techniques can derive clinical and scientific impact from these messy,
incomplete, and partial data. We seek submissions on topics including
* Health monitoring systems
* Clinical data labeling
* Clustering and Phenotype discovery
* Clinical outcome prediction and tools for personalized medicine
* Efficient, scalable processing of clinical data
* Feature selection and dimensionality reduction in clinical data
* Timeseries analysis with medical applications
Submission Details:
Poster submissions should be extended abstracts no more than 4 pages in
length (in NIPS format, do not need to be anonymous). Extended abstracts
should be submitted by October 9 11:59 PM PDT via email to
[email protected]
Important Dates:
Submission - 9th October 2013 11:59 PM PDT
Notification - 23rd October 2013
Workshop - 9th or 10th December 2013
Organizers:
Jenna Wiens (MIT)
Finale Doshi-Velez (Harvard Medical School)
Madalina Fiterau (CMU)
Can Ye (CMU)
Le Lu (NIH)
Balaji Krishnapuram (Siemens Healthcare)
Shipeng Yu (Siemens Healthcare)
If you have questions about the workshop or the submission process please
contact the organizers [email protected]
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