@conference {BT205,
	title = {BT205: Text Mining for Drug Development: Gathering Insights to Support Decision Making},
	booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)},
	series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)},
	year = {2016},
	month = {11/30/16},
	publisher = {CEUR-ws.org Volume 1747},
	organization = {CEUR-ws.org Volume 1747},
	abstract = {<p>Drug discovery in Pharma R\&amp;D is an information driven process requiring many disparate bits of data from many different sources, both structured and unstructured. Text mining is the key methodology used to extract entities and relationships from unstructured text in the quest for the knowledge needed to bring a safe and effective drug to market and beyond. Much of the insight needed in early drug research to identify drug target to disease relationships and progress a potential drug target, comes from published literature and internal reports. Later stage drug development requires many additional sources of information including case reports, clinical trials, competitive intelligence and other diverse sources. In this publication, I will present 4 different use cases on how text mining is used to drive decision making in drug discovery and development and also how it can be used to identify patient insights from sources such as social media.</p>
},
	url = {http://ceur-ws.org/Vol-1747/BT205_ICBO2016.pdf},
	author = {Sherri Matis-Mitchell}
}
