Markov logic networks for adverse drug event extraction from text

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Natarajan, S (Author) Other; Bangera, V (Author) Other; Khot, T (Author) Other; Picado, J (Author) Other; Wazalwar, A (Author) Other; Costa, VS (Author) FCUP; Page, D (Author) Other; Caldwell, M (Author) Other

Journal

Title: Knowledge and Information Systems

Vol. 51

Pages: 435-457

ISSN: 0219-1377

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00K-R4F

DOI: 10.1007/s10115-016-0980-6

Abstract (EN): Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work, we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23