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P Tafti A, Badger J, LaRose E, et al. Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR Med Inform. 2017;5(4):e51doi: 10.2196/medinform.9170.
P Tafti, A., Badger, J., LaRose, E., Shirzadi, E., Mahnke, A., Mayer, J., Ye, Z., Page, D., & Peissig, P. (2017). Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR medical informatics, 5(4), e51. https://doi.org/10.2196/medinform.9170
P Tafti, Ahmad, et al. "Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure." JMIR medical informatics vol. 5,4 (2017): e51. doi: https://doi.org/10.2196/medinform.9170
P Tafti A, Badger J, LaRose E, Shirzadi E, Mahnke A, Mayer J, Ye Z, Page D, Peissig P. Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR Med Inform. 2017 Dec 08;5(4):e51. doi: 10.2196/medinform.9170. PMID: 29222076; PMCID: PMC5741828.
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JMIR Med Inform. 2017 Dec 08;5(4):e51. doi: 10.2196/medinform.9170.

Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.

JMIR medical informatics

Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig

Affiliations

  1. Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States.
  2. Institute of Electrical and Electronics Engineers, Dublin, Ireland.
  3. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.

PMID: 29222076 PMCID: PMC5741828 DOI: 10.2196/medinform.9170

Abstract

BACKGROUND: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs.

OBJECTIVE: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs.

METHODS: We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs.

RESULTS: The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions.

CONCLUSIONS: To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.

©Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.12.2017.

Keywords: adverse drug event; adverse drug reaction; drug side effects; machine learning; text mining

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