| Title |
Resources for comparing the speed and performance of medical autocoders
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, June 2004
|
| DOI | 10.1186/1472-6947-4-8 |
| Pubmed ID | |
| Authors |
Jules J Berman |
| Abstract |
Concept indexing is a popular method for characterizing medical text, and is one of the most important early steps in many data mining efforts. Concept indexing differs from simple word or phrase indexing because concepts are typically represented by a nomenclature code that binds a medical concept to all equivalent representations. A concept search on the term renal cell carcinoma would be expected to find occurrences of hypernephroma, and renal carcinoma (concept equivalents). The purpose of this study is to provide freely available resources to compare speed and performance among different autocoders. These tools consist of: 1) a public domain autocoder written in Perl (a free and open source programming language that installs on any operating system); 2) a nomenclature database derived from the unencumbered subset of the publicly available Unified Medical Language System; 3) a large corpus of autocoded output derived from a publicly available medical text. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 22% |
| Professor | 2 | 22% |
| Researcher | 2 | 22% |
| Student > Bachelor | 1 | 11% |
| Student > Postgraduate | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 33% |
| Medicine and Dentistry | 3 | 33% |
| Agricultural and Biological Sciences | 1 | 11% |
| Biochemistry, Genetics and Molecular Biology | 1 | 11% |
| Unknown | 1 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 15 April 2014.
All research outputs
#18,370,767
of 22,753,345 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,567
of 1,985 outputs
Outputs of similar age
#51,527
of 54,452 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 5 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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 54,452 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.