Rachel Mason Ginn

Rachel Mason Ginn
Arizona State University | ASU · School of Computing, Informatics, and Decision Systems Engineering

MS (2015) / BSE (2012)

About

10
Publications
14,922
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
1,363
Citations
Citations since 2017
2 Research Items
1150 Citations
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200
2017201820192020202120222023050100150200

Publications

Publications (10)
Article
Full-text available
We developed an electronic records methodology to programmatically estimate the date of first appearance of coccidioidomycosis symptoms in patients. We compared the diagnostic delay with overall healthcare utilization charges. Many patients (46%) had delays in diagnosis of >1 month. Billed healthcare charges before diagnosis increased with length o...
Article
Full-text available
Introduction Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Objectives Our primary aims were to assess the possibility of utili...
Article
Full-text available
Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expresse...
Article
Full-text available
Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resour...
Conference Paper
Full-text available
Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied-with health related forums and community support groups preferred for the task. We present a systematic study of tweets collected for 74 drugs to assess their value as sources...
Article
Full-text available
Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied - with health related forums and community support groups preferred for the task. We present a systematic study of tweets collected for 74 drugs to assess their value as sourc...
Conference Paper
Full-text available
The recent popularity of health related social networks has enabled users to communicate about drugs, treatments and other health related issues over the Internet, making it a rich resource for monitoring drugs after they hit the market. In this paper we explore a novel probabilistic model for drug categorization using a two-step approach. We first...
Article
Full-text available
Finding gene functions discussed in the literature is an important task of information extraction (IE) from biomedical documents. Automated computational methodologies can significantly reduce the need for manual curation and improve quality of other related IE systems. We propose an open-IE method for the BioCreative IV GO shared task (subtask b),...

Network

Cited By

Projects

Project (1)
Project
The overarching goal of this project is to deploy the infrastructure needed to explore the value of informal social network postings as a source of “signals” of potential adverse drug reactions soon after the drugs hit the market, paying particular attention at the value such information might have to detect adverse events earlier than currently possible, and to detect effects not easily captured by traditional means.