@inproceedings{artz:14a,
	abstract = {One of the challenges analyzing medical conditions in unstructured data is in determining whether they are reported as indications or side effects. Classifying different types of medical conditions enables a deeper understanding of disease manifestation and treatment risk, helping further research in this field. In our study, we collect medical forum posts, annotate different types of medical conditions, and classify medical conditions into indications and side effects using natural language processing and machine learning techniques.},
	address = {Washington, DC},
	author = {Artz, Nathan and Choi, Jinho D. and Sawyer, James},
	booktitle = {Proceedings of the American Medical Informatics Association Annual Symposium},
	date-added = {2017-08-31 03:01:32 +0000},
	date-modified = {2018-08-21 18:39:07 +0000},
	keywords = {selected,medical},
	pages = {1315},
	series = {AMIA'14},
	title = {{Finding Different Types of Medical Conditions: From Data Generation to Automatic Classification}},
	url = {https://knowledge.amia.org/56638-amia-1.1540970/t-005-1.1543914/f-005-1.1543915/a-247-1.1544930/an-247-1.1544931?qr=1},
	url_slides = {https://www.slideshare.net/jchoi7s/finding-different-types-of-medical-conditions-from-data-generation-to-automatic-classification},
	year = {2014},
	Bdsk-Url-1 = {http://knowledge.amia.org/56638-amia-1.1540970/t-005-1.1543914/f-005-1.1543915/a-247-1.1544930/an-247-1.1544931?qr=1}}

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