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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Exploring Spanish health social media for detecting drug effects
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, June 2015
|
| DOI | 10.1186/1472-6947-15-s2-s6 |
| Pubmed ID | |
| Authors |
Isabel Segura-Bedmar, Paloma Martínez, Ricardo Revert, Julián Moreno-Schneider |
| Abstract |
Adverse Drug reactions (ADR) cause a high number of deaths among hospitalized patients in developed countries. Major drug agencies have devoted a great interest in the early detection of ADRs due to their high incidence and increasing health care costs. Reporting systems are available in order for both healthcare professionals and patients to alert about possible ADRs. However, several studies have shown that these adverse events are underestimated. Our hypothesis is that health social networks could be a significant information source for the early detection of ADRs as well as of new drug indications. In this work we present a system for detecting drug effects (which include both adverse drug reactions as well as drug indications) from user posts extracted from a Spanish health forum. Texts were processed using MeaningCloud, a multilingual text analysis engine, to identify drugs and effects. In addition, we developed the first Spanish database storing drugs as well as their effects automatically built from drug package inserts gathered from online websites. We then applied a distant-supervision method using the database on a collection of 84,000 messages in order to extract the relations between drugs and their effects. To classify the relation instances, we used a kernel method based only on shallow linguistic information of the sentences. Regarding Relation Extraction of drugs and their effects, the distant supervision approach achieved a recall of 0.59 and a precision of 0.48. The task of extracting relations between drugs and their effects from social media is a complex challenge due to the characteristics of social media texts. These texts, typically posts or tweets, usually contain many grammatical errors and spelling mistakes. Moreover, patients use lay terminology to refer to diseases, symptoms and indications that is not usually included in lexical resources in languages other than English. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 3 | 27% |
| India | 2 | 18% |
| United States | 2 | 18% |
| United Kingdom | 1 | 9% |
| Venezuela, Bolivarian Republic of | 1 | 9% |
| Unknown | 2 | 18% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 55% |
| Practitioners (doctors, other healthcare professionals) | 3 | 27% |
| Scientists | 2 | 18% |
Mendeley readers
The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 116 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 23 | 20% |
| Researcher | 16 | 14% |
| Student > Ph. D. Student | 15 | 13% |
| Other | 10 | 9% |
| Student > Bachelor | 8 | 7% |
| Other | 16 | 14% |
| Unknown | 28 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 19% |
| Computer Science | 20 | 17% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 6% |
| Nursing and Health Professions | 5 | 4% |
| Engineering | 5 | 4% |
| Other | 19 | 16% |
| Unknown | 38 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 August 2019.
All research outputs
#4,471,352
of 22,813,792 outputs
Outputs from BMC Medical Informatics and Decision Making
#394
of 1,988 outputs
Outputs of similar age
#56,615
of 264,259 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 37 outputs
Altmetric has tracked 22,813,792 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,988 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,259 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.