Ima Seminar Series 2012/2013 Jenna Reps: a Novel Algorithm to Detect Rare Prescription Side Effects
نویسنده
چکیده
Conventional algorithms that generate signals for potential side effects may require many thousands of patients to take a drug before producing a signal for a rare side effect and it is even possible that they will never generate a signal for some rare side effects. As these rare side effects often result in patient morbidities or mortalities it is important to identify them efficiently. In this presentation I will summarise a longitudinal dataset known as The Health Improvement Network database and discuss the potential of discovering rare side effects by mining this database. I will then describe a novel semisupervised algorithm that uses known common side effects to train a model that can be used to identify the rare side effects and present tentative results.
منابع مشابه
Detecting adverse drug reactions in the general practice healthcare database
The novel contribution of this research is the development of a supervised algorithm that extracts relevant attributes from The Health Improvement Network database to detect prescription side effects. Prescription drug side effects are a common cause of morbidity throughout the world. Methods that aim to detect side effects have historically been limited due to the data available, but some of t...
متن کاملComparison of algorithms that detect drug side effects using electronic healthcare databases
The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare database containing medical information on over 11 million patients that has excellent potential for detecting ADRs. In this paper we apply four exist...
متن کاملReps, Jenna M. and Garibaldi, Jonathan M. and Aickelin, Uwe and Soria, Daniele and Gibson, Jack E. and Hubbard, Richard B. (2014) Signalling paediatric side effects using an ensemble of simple study
Background: Children are frequently prescribed medication ‘off-label’, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions that prevent children from being included in the majority of clinical trials. Methods: Multiple measures of association are calculated for e...
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Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner. Unfortunately, the ability of these databases to be used for causal inference is limited due to the passive way in which the data are collected resulting in various forms of bias. In this paper we investigate a method that can overcome these limitations and determine causal cont...
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The pharmaceutical industry is plagued by the problem of side effects that can occur anytime a prescribed medication is ingested. There has been a recent interest in using the vast quantities of medical data available in longitudinal observational databases to identify causal relationships between drugs and medical events. Unfortunately the majority of existing post marketing surveillance algor...
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