Adverse Drug Reactions are the biggest safety concern in the health field and they refer to harmful and unintended effects of drugs administered for the prevention and treatment of illness, both at normal dosages and in cases of incorrect usage or errors in medication. They are the fourth highest cause of death for patients in U.S. hospitals and  up to 15 percent of hospital expenses are due to drug-related complications despite the fact that clinical trials are larger, longer and more expensive than ever and pharmacovigilance area is high.

The reason there can still be adverse drug reactions despite a billion dollars in clinical trials is because no trial can account for everything.  Due to the media publicity and trials when there are adverse reactions, more people than ever are unwilling to participate in clinical trials, which drugs for kids faring the worse due to lack of participation. Sometimes if there are adverse effects, they won't show up in a limited sample size and will only be found after they are in broad use. Regulatory agencies monitor adverse reactions once the drug is on the market, and the main tool at their disposal is a system of voluntary notification whereby medical professionals and patients can report suspected reactions, but they are not used much - under 20 percent of incidents are reported. 

Researchers at Carlos III Universidad de Madrid (UC3M) have developed a system for detecting adverse effects of pharmaceutical drugs by tracking information generated by patients on specialized blogs or social networks such as Twitter in real time.


Credit: uc3m

The researchers explain that online health information searches are the third most popular activity in Google, with 170,000 searches performed every 5 seconds. Isabel Segura Bedmar of the UC3M Computer Science and Engineering Department points out that, “There is a lot of user-generated information these days, so social networks can be a valuable source of information on adverse effects of pharmaceutical drugs after the clinical trial stage is over and the drug is on the market.”

The large amount and variety of information to be stored, as well as its rapidly changing nature, make this a typical big data problem.

The prototype, created by these scientists within the framework of the European research Project TrendMiner, makes it possible to analyze the comments on social media by using natural language processing techniques (NLP). Thanks to these techniques, patients’ colloquial descriptions are “translated” into manageable data in comparatives studies which allow us to identify patterns and trends.”



This data may also be combined with data from other sources, such as patients’ electronic medical records, which contain very useful information about diagnosis, treatments, etc. Most of this information is also expressed in natural language, which means that it must be codified and turned into structured information in order to be able to work with it,” explains another of the researchers, Professor Paloma Martínez, of the Advanced Databases Laboratory at UC3M.

The prototype created to analyze comments on social networks contains a linguistic processor based on the Daedalus company’s MeaningCloud, a commercial technology for the analysis of big data which recognizes mentions of pharmaceutical drugs, adverse effects and illnesses. The system displays the development of these references and their “co-occurrences” i.e., it registers which drugs are mentioned and what the adverse effects are. For example, the system monitors anti-anxiety drugs and to do so it takes into account not only the references to the active ingredient or generic name of the drugs in this category (among others lorazepam and diazepam) but also commercial brand names (such as Orfidal). In addition, all of these drug references may also be analyzed in relation to their therapeutic effects (such as Orfidal being indicated for anxiety) and their adverse effects (such as Orfidal possibly causing shaking and tremors).

This technology could also be used by a pharmaceutical company in order to “find out what people are saying about a drug, for example, or to gather information on suspicions of adverse effects of drugs to supplement notification received through traditional channels,” comments José Luis Martínez Fernández, who combines teaching in the UC3M Computer Science and Engineering Department with his work as Consulting Director at Daedalus. There are parts of medical reports, notes or clinical histories “which are difficult to process, and because of this they are not being worked on; this technique could help us to get the most out of this content,” he explains. “The challenge is to transform these texts, which are currently stored without being analyzed, into structured information, which allows them to be used for clinical and epidemiological purposes to gain new knowledge or to analyze trends which aid decision-making,” he stresses.

Citation: Isabel Segura-Bedmar, Paloma Martínez, Ricardo Revert y Julián Moreno-Schneider (2015). Exploring Spanish Health Social Media for detecting drug effects. BMC Medical Informatics and Decision Systems. Accepted for publication.