Markov logic networks for adverse drug event extraction from text

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

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

Revista

Título: Knowledge and Information Systems

Vol. 51

Páginas: 435-457

ISSN: 0219-1377

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 9 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 23