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Text mining with automation becomes boon for ADR reporting
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Laxmi Yadav, Mumbai
April 03 , 2017
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Text mining with automation has emerged as a powerful tool for
pharmaceutical industry helping them report adverse drug reactions
(ADRs) to regulatory authority in a qualitative, efficient and cost
effective manner, according to Kailash Chanduka, Director- Testing
Shared Services at Aris Global Software Pvt Ltd.
Text mining is
the process of retrieving relevant information from large amounts of
both structured and unstructured text in electronic health record as
well as unstructured text in social media, internet search history,
biomedical literature and product information documents with the help of
automated pattern learning and analyzing it with the help of machine
learning and marking temporal relation between drug and event.
Regulatory
authorities and pharma companies' interest in these secondary sources
of adverse drug reactions are growing due to extensive under reporting
of adverse drug reactions by medical professionals, inadequate clinical
data etc.
Social media platforms are increasingly being used by
patients and their caregivers to discuss health issues including adverse
drug reactions. Hence these platforms are potentially an interesting
source of health data. Global regulatory authorities such as US FDA, EMA
and pharma companies are looking for social media platforms to get
safety profile of drugs.
The Web-RADR project started in
September 2014 to use social media for drug safety data and is funded by
the Innovative Medicines Initiative, which in turn is funded by the
European Commission and the European Federation of Pharmaceutical
Industries and Associations. Drug regulators, pharmaceutical companies
the World Health Organization and universities are part of the project.
European
Medical Agency (EMA) along with drug makers will analyze the drug
safety data from social media forums captured by the Web-RADR data
mining tools to take a call on its use.
All data from social
media is added to the EU’s pharmacoviligance ADR database,
EudraVigilance, which is accessible to patients and researchers, to help
signal detection.
Funded by the US FDA, Epidemico, a US based
company conducted a study in 2014 which looked into 6.9 million Twitter
posts and found 4,401 tweets resembling an ADR. The study found a high
consistency between the data across organ classes while comparing with
data held by the FDA. Epidemico uses its Med-WatcherSocial platform to
detect ADRs from social media posts for around 1,400 drugs.
Indian
Pharmacopoeia Commission (IPC) has also started mobile app in
vernacular languages and helpline for patients to report ADRs. Companies
like Novartis, Treato, IMS Health started looking into posts from
social media to find potential drug safety signals.
Monitoring
social media will not replace the traditional ADR reporting systems such
as clinical trials, post-marketing spontaneous reporting and patient
registries that can monitor safety, he said.
Besides social media, electronic health records are also used for collecting drug safety data.
The
use of text mining tools and techniques to leverage secondary data
resources will pave way for proactive holistic pharmacovigilance to meet
the goal of identifying the adverse events as soon (speed) and
accurately (precision) as possible leading to improved patient safety,
said Chanduka.
However there are text mining challenges in
secondary data source which can be categorised into data source
challenges – acquisition, normalisation, cleansing, filtering of data;
Operational challenges-- heterogenecity of medical text, colloquial
terms of patients, vocabulary quality & completeness; Context of
medical terms including negation; scalable and secure systems for
electronic health record.
Despite the challenges, in recent years
it has become clear that additional sources and data-mining techniques
have been proven useful to obtain drug safety information which could
contribute to decreasing the impact of ADRs on patient well-being, he
concluded.
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