Connectionists: AAAI Workshop on Modern AI for Health: Deadline Extended!

[Finale P Doshi]

AAAI Workshop on Modern Artificial Intelligence for Health Analytics

==== Call for Abstracts ====

When: July 27th or July 28th during AAAI Workshops
Where: Québec City, QC, Canada

Website: http://www.cebm.brown.edu/maiha
Email: maiha.aaai at gmail.com

Important Dates:
Submission - 4/24/2014 11:59 PM EST
Notification - 5/1/2014
Workshop - 7/27/14 or 7/28/14

The proliferation of health-related information presents unprecedented
opportunities to improve patient care. However, medical experts are currently
overwhelmed by information, and existing artificial intelligence (AI)
technologies are often inadequate for the challenges associated with analyzing
clinical data. Novel computational methods are needed to process, organize, and
make sense of these data. The objective of this workshop is to discuss
computational methods that transform healthcare data into knowledge that
ultimately improves patient care. Moreover, this workshop will focus on
community building, by bringing together AI researchers interested in health
and physicians interested in AI. The workshop will include a structured
discussion around venues for this sort of emerging, interdisciplinary work. It
will also include invited talks by leaders in the field. 

We solicit papers in three tracks (further detailed below): (1) novel AI
methods for healthcare, (2) clinical applications of AI, and (3) open AI
challenges in health.  We are particularly interested in work done in
collaboration with clinicians and clinical researchers. Potential topics
include:

* Machine Learning
        - Predicting individual patient outcomes from past data
        - Patient risk stratification and clustering
        - Handling core methodological challenges (e.g., data sparsity)
        - Exploiting resources to augment training data

* Computer Vision
        - Vitals monitoring
        - Medical imaging
        - Brain imaging (fMRI)

* Natural language processing
        - Extracting structured data from free-text (e.g., clinical notes)
        - Biomedical text classification
        - Parsing biomedical literature

* Information retrieval and organization
        - Identifying relevant literature
        - Clinical question answering
        - Ontology learning
We define healthcare information broadly, including heterogeneous data such as
clinical trial results, patient health records, genomic data, wearable health
monitor outputs, online physician reviews, and medical images.

Submissions may be up to 4 pages (plus references) and must be aligned to one
of the following tracks:

(1) Methods. Should present a novel methodology for a health sciences problem.
While the application need not be unsolved, the method(s) must be specifically
relevant to healthcare data.

(2) Applications. Should describe a real-world application of AI methods to
healthcare data. Methods need not be novel, but they should be
state-of-the-art. These papers will be judged on the significance of the
application.

(3) Open problems. Should describe open problems/datasets that might interest
AI researchers.  Preference will be given to submissions that describe problems
for which data are readily available.

Submissions should be no longer than 4 pages plus references. They should be
emailed directly to: maiha.aaai at gmail.com.

Organizers:
* Byron Wallace, Brown University (byron_wallace at brown.edu)
* Jenna Wiens, MIT (jwiens at csail.mit.edu)
* Finale Doshi-Velez, Harvard Medical School (finale at mit.edu)
* David Kale, USC/Children's Hospital Los Angeles (dkale at usc.edu)