NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE1.0)
hosted by University of Massachusetts Medical School
Adverse drug events (ADEs) are common and occur in approximately 2-5% of hospitalized adult patients. Each ADE is estimated to increase healthcare cost by more than $3,200. Severe ADEs rank among the top 5 or 6 leading causes of death in the United States. Prevention, early detection and mitigation of ADEs could save both lives and dollars. Employing natural language processing (NLP) techniques on electronic health records (EHRs) provides an effective way of real-time pharmacovigilance and drug safety surveillance.
We’ve annotated 1092 EHR notes with medications, as well as relations to their corresponding attributes, indications and adverse events. It provides valuable resources to develop NLP systems to automatically detect those clinically important entities. Therefore we are happy to announce a public NLP challenge, MADE1.0, aiming to promote deep innovations in related research tasks, and bring researchers and professionals together exchanging research ideas and sharing expertise. The ultimate goal is to further advance ADE detection techniques to improve patient safety and health care quality. -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 3089 bytes Desc: not available URL: <https://www.uib.no/mailman/public/corpora/attachments/20171120/46f3000f/attachment.txt>