Category Archives: Healthcare Industry

Posts about the healthcare industry.

Could Antidepressants Be the Cause of Birth Defects?

We agree that it is not typical at all for an Information Technology company to talk about antidepressants and pregnancy in its own blog. But here at MeaningCloud we have realized that health issues have a great impact on social networks, and the companies from that industry, including pharmas, should try to understand the conversation which arises around them. How? Through text analysis technology, as discussed below.

Looking at the data collected by our prototype for monitoring health issues in social media, we were surprised by the sudden increase in mentions of the term ‘pregnancy’ on July 10. In order to understand the reason of this fact, we analyzed the tweets related to pregnancy and childbearing. It turned out that the same day a piece of news on a study issued by the British Medical Journal about the harmful effects that antidepressants can have on the fetus had been published.
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Exploring Social Media for Healthcare Data

People enjoy sharing information through social media, including healthcare data. Yeah, it is true! And it constitutes the starting point of the research work titled ‘Exploring Spanish health social media for detecting drug effects’, which aims at following social media conversations to identify how people talk about their relation with drug consumption. This allows identifying possible adverse effects previously unknown related to these drugs. Although there is a protocol to communicate to the authorities the identification of a drug adverse effect, only a 5 – 20% of them are reported. Besides, conversations around drugs, symptoms, conditions and diseases can be analyzed to learn more about them. For example, it is possible to see how people search for specific drugs using social media, while others sell them, perhaps illegally. Many others talk about mixing alcohol with drugs or other illegal substances. Of course, one cannot believe everything that appears on the Internet this is another issue—, but it can highlight some hypothesis for further research.


Some researchers from the Advanced Databases Group at Carlos III University of Madrid have carried out the mentioned study, designing hybrid models to capture the needed knowledge to identify adverse effects. The Natural Language Processing platform which supports the implementation of the analysis process based on such models is MeaningCloud. The customization capabilities provided by the platform have been decisive to include specific vocabulary and medical domain knowledge. As we know, the names of drugs and symptoms might be complex and, in some cases, difficult to write properly. The algorithm’s results are promising, with a 10% increase in recall when compared to other known algorithms. You can find further details in the scientific paper published by the BMC Medical Informatics and Decision Making Journal.

These developments have been part of the TrendMiner project, and are now available in the prototype website TrendMiner Health Analytics Dashboard, which shows people’s comments about antidepressants gathered from social media. The console displays the mentions of antidepressants and related symptoms and, by clicking on any of them, their evolution over time. Moreover, the source texts analyzed to compute those mentions are shown at the bottom, with labels highlighting the names of drugs, symptoms or diseases, and any relations among them. Such relations might say if a drug is indicated for a symptom or if a disease is an adverse effect of the mentioned drug. The prototype also allows searching by the ATC code (Anatomical Therapeutic Chemical Classification System) and the corresponding level according to this classification scheme. So, if you mark the ‘By Active Substance’ selector, you are searching any drug containing the active substance of the product you inserted in the search box. Furthermore, the predictive search functionality makes easier to find the right expression for a drug or disease. Please, have a look at the prototype and tell us what you think about it. If you find a chart useful, you can even tweet it from there! Any comment is more than welcome.

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.