| Title |
Recognition of medication information from discharge summaries using ensembles of classifiers
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2012
|
| DOI | 10.1186/1472-6947-12-36 |
| Pubmed ID | |
| Authors |
Son Doan, Nigel Collier, Hua Xu, Pham Hoang Duy, Tu Minh Phuong |
| Abstract |
Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 6% |
| Canada | 2 | 3% |
| United Kingdom | 1 | 2% |
| Argentina | 1 | 2% |
| Ireland | 1 | 2% |
| Unknown | 57 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 14 | 21% |
| Student > Ph. D. Student | 12 | 18% |
| Researcher | 10 | 15% |
| Professor > Associate Professor | 4 | 6% |
| Student > Bachelor | 3 | 5% |
| Other | 11 | 17% |
| Unknown | 12 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 17 | 26% |
| Medicine and Dentistry | 16 | 24% |
| Agricultural and Biological Sciences | 4 | 6% |
| Mathematics | 2 | 3% |
| Nursing and Health Professions | 2 | 3% |
| Other | 8 | 12% |
| Unknown | 17 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 24 May 2012.
All research outputs
#12,853,846
of 22,664,644 outputs
Outputs from BMC Medical Informatics and Decision Making
#875
of 1,978 outputs
Outputs of similar age
#87,751
of 163,426 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 40 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 53% of its peers.
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We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.