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 mlhealthcare.nips2013@gmail.com

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 mlhealthcare.nips2013@gmail.com