Adverse effects of medications and social media monitoring

Adverse Drug Reactions (ADR) are the biggest safety concern in the health field. Adverse Drug Reactions 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. ADRs are the fourth cause of death for patients in hospitals in the U.S. Therefore, the pharmacovigilance area is receiving a great deal of attention at the moment, due to the high incidence of ADRs and the high associated costs (between 15 and 20 percent of hospital expenses are due to drug-related complications.)

There are certain adverse drug reactions which are not discovered during clinical trials because they do not become known until the drug has been on the market for several years. Therefore, medicine regulatory agencies have to monitor ADRs 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 ADRs (in Spain patients have been able to do so since July 2012). However, these systems are hardly used, and estimates indicate that only 5-20% of ADRs are reported, either due to lack of time, the complexity of the process, lack of knowledge of ADRs or poor coordination among healthcare staff.

As part of the European TrendMiner project, a prototype to analyze comments on social networks has been built that features MeaningCloud semantic analysis to recognize 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).

To read more about this project, developed with the Universidad Carlos III de Madrid go to the university’s website.

We have now a dedicated business exclusively focused on the health and pharmaceutical sectors

Konplik.Health begins operations with the health-related assets from MeaningCloud, including its leading natural language processing, deep semantic analysis, AI platform, and adaptations specific to the life sciences.

Health and pharma companies can exploit their unstructured information

There are new kinds of data that are specific to the
healthcare and pharmaceutical industries
(such as electronic health records) as well as data science tools that allow us to extract valuable knowledge from that data.


With MeaningCloud, it is possible to identify the costs of medical treatments, their efficiency (cost, benefits, and risks), references to drugs, side effects, or long-term results. That is why our text analytics solution for the healthcare and pharma domains has so much potential.

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