Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.

By Xiaoyan Wang, George Hripcsak, M. Markatou, Carol Friedman. JAMIA Journal of the American Medical Informatics Association, 2009

299 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Toxicology

View Paper: https://www.semanticscholar.org/paper/1326de90dd9a7db7a0c03941c696fe0c98373399

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Abeed Sarker
2. Graciela Gonzalez
3. Yuan Luo
4. Lars Juhl Jensen
5. Siddhartha R. Jonnalagadda

In Molecular Biology

1. Joshua Charles Denny
2. Zexian Zeng
3. Yuan Luo
4. Abeed Sarker
5. Lars Juhl Jensen
6. Graciela Gonzalez
7. Søren Brunak
8. Justin B. Starren

In Toxicology

1. Abeed Sarker
2. Graciela Gonzalez
3. Peter Bjødstrup Jensen
4. Lars Juhl Jensen
5. Søren Brunak

Citations in the corpus

• Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register (2015, methodology): “Wang et al (2009) [12] conducted a study using notes from the inpatient department of Presbyterian Hospital, New York.”
• Extraction of potential adverse drug events from medical case reports (2012, methodology): “[3] who applied the MedLEE system on discharge summaries to identify medication events and entities that could be potential adverse drug effects; these were detected using the”
• Mining electronic health records: towards better research applications and clinical care (2012, background): “As an alternative, mining of medication and ADE data that are captured routinely in EHRs is being explored as an approach to rapidly uncover drug–ADE associations 45 .”
• Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, background): “Wang et al. [54, 55], Friedman [56], and Haerian et al. [57] used MedLEE to recognize named entities such as medications and potential adverse events (e.g., diseases and symptoms) in unstructured data from hospital records.”
• PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations (2010, background): “Contact: josh.denny@vanderbilt.edu Received and revised on February 2, 2010; accepted on March 17, 2010”
• Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training (2014, background): “NLP approaches have been proposed to detect ADRs from text, and they have mostly focused on utilizing data from Electronic Health Records (EHR) [15, 16, 17], and also other sources such as clinical reports [6, 10].”