Ensemble method–based extraction of medication and related information from clinical texts | Zendy

Young-Jun Kim | Zendy; Stéphane M. Meystre | Zendy

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Ensemble method–based extraction of medication and related information from clinical texts

Author(s) -

Young-Jun Kim,

Stéphane M. Meystre

Publication year - 2019

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocz100

Subject(s) - computer science , artificial intelligence , machine learning , f1 score , information extraction , relationship extraction , clinical decision support system , support vector machine , recall , task (project management) , generalization , test set , precision and recall , ensemble learning , natural language processing , data mining , decision support system , mathematical analysis , linguistics , philosophy , mathematics , management , economics

Accurate and complete information about medications and related information is crucial for effective clinical decision support and precise health care. Recognition and reduction of adverse drug events is also central to effective patient care. The goal of this research is the development of a natural language processing (NLP) system to automatically extract medication and adverse drug event information from electronic health records. This effort was part of the 2018 n2c2 shared task on adverse drug events and medication extraction.

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