Deep Learning and Natural Language Processing

First off, I would like to say that an abstract I co-authored, titled Improving Lupus Phenotyping Using Natural Language Processing, has been accepted to the 2015 Summit on Translational Bioinformatics. This conference is in San Francisco during late March. I will not be attending as I will be busy with classes (two of the PI's with which I am working will be attending), but I am still heavily working on information to be presented for the poster symposium then.

My most recent advances in this research have brought me to attempting to unravel the intricacies behind Deep Learning. Our goal is to classify patients', based on digitized doctors' notes for those patients, status of Lupus (effectively present or not). The ramifications of such research would result in quicker, easier, and more accurate recruitment for clinical trials, as well as the outperformance of solely using icd-9 billing codes as a classifier.

Some sources consulted for research:

• http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/
• http://deeplearning.stanford.edu/tutorial/
• https://www.youtube.com/watch?v=n1ViNeWhC24
• http://arxiv.org/pdf/1206.5533.pdf
• http://www.socher.org/uploads/Main/PaulusSocherManning_NIPS2014.pdf
• http://nlp.stanford.edu/~socherr/thesis.pdf
• http://nlp.stanford.edu/~socherr/SocherChenManningNg_NIPS2013.pdf
• http://www.aclweb.org/anthology/P/P12/P12-1092.pdf
• http://www.aaai.org/Papers/JAIR/Vol37/JAIR-3705.pdf