| Title |
Integration of an OWL-DL Knowledge Base with an EHR Prototype and Providing Customized Information
|
|---|---|
| Published in |
Journal of Medical Systems, July 2014
|
| DOI | 10.1007/s10916-014-0075-4 |
| Pubmed ID | |
| Authors |
Xia Jing, Stephen Kay, Tom Marley, Nicholas R. Hardiker |
| Abstract |
When clinicians use electronic health record (EHR) systems, their ability to obtain general knowledge is often an important contribution to their ability to make more informed decisions. In this paper we describe a method by which an external, formal representation of clinical and molecular genetic knowledge can be integrated into an EHR such that customized knowledge can be delivered to clinicians in a context-appropriate manner.Web Ontology Language-Description Logic (OWL-DL) is a formal knowledge representation language that is widely used for creating, organizing and managing biomedical knowledge through the use of explicit definitions, consistent structure and a computer-processable format, particularly in biomedical fields. In this paper we describe: 1) integration of an OWL-DL knowledge base with a standards-based EHR prototype, 2) presentation of customized information from the knowledge base via the EHR interface, and 3) lessons learned via the process. The integration was achieved through a combination of manual and automatic methods. Our method has advantages for scaling up to and maintaining knowledge bases of any size, with the goal of assisting clinicians and other EHR users in making better informed health care decisions. |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 34 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 22% |
| Researcher | 6 | 17% |
| Student > Postgraduate | 5 | 14% |
| Student > Ph. D. Student | 4 | 11% |
| Professor > Associate Professor | 3 | 8% |
| Other | 6 | 17% |
| Unknown | 4 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 31% |
| Medicine and Dentistry | 9 | 25% |
| Social Sciences | 4 | 11% |
| Agricultural and Biological Sciences | 2 | 6% |
| Engineering | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 6 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 10 September 2014.
All research outputs
#6,407,124
of 22,763,032 outputs
Outputs from Journal of Medical Systems
#220
of 1,143 outputs
Outputs of similar age
#61,038
of 227,473 outputs
Outputs of similar age from Journal of Medical Systems
#6
of 16 outputs
Altmetric has tracked 22,763,032 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,143 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 79% 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 227,473 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.