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Operationalizing Semantic Medline for meeting the information needs at point of care.

Rastegar-Mojarad M, Li D, Liu H - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Motivated by the recent advance in semantically enhanced information retrieval, we have developed a system, which aims to bring semantically enriched literature, Semantic Medline, to meet the information needs at point of care.This study reports our work towards operationalizing the system for real time use.We demonstrate that the migration of a relational database implementation to a NoSQL (Not only SQL) implementation significantly improves the performance and makes the use of Semantic Medline at point of care decision support possible.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.

ABSTRACT
Scientific literature is one of the popular resources for providing decision support at point of care. It is highly desirable to bring the most relevant literature to support the evidence-based clinical decision making process. Motivated by the recent advance in semantically enhanced information retrieval, we have developed a system, which aims to bring semantically enriched literature, Semantic Medline, to meet the information needs at point of care. This study reports our work towards operationalizing the system for real time use. We demonstrate that the migration of a relational database implementation to a NoSQL (Not only SQL) implementation significantly improves the performance and makes the use of Semantic Medline at point of care decision support possible.

No MeSH data available.


The distribution of response time for 2750 queries
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f3-2092053: The distribution of response time for 2750 queries

Mentions: We implemented our systems in Java and compared response time for 2,750 queries for two approaches. These queries were asked by clinicians with no hits in AME. To get the top five relevant sentences for each of these queries, the new approach took 5 minutes and 19 seconds total. The average response time for each query was about 116 milliseconds with the median response time less than 100 milliseconds. Table 1 shows ten of these queries, response time, and three top relevant sentences retrieved by the new system. In addition, the response time for the previous system is mentioned. Figure 3 illustrates the distribution of response time for the queries.


Operationalizing Semantic Medline for meeting the information needs at point of care.

Rastegar-Mojarad M, Li D, Liu H - AMIA Jt Summits Transl Sci Proc (2015)

The distribution of response time for 2750 queries
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525258&req=5

f3-2092053: The distribution of response time for 2750 queries
Mentions: We implemented our systems in Java and compared response time for 2,750 queries for two approaches. These queries were asked by clinicians with no hits in AME. To get the top five relevant sentences for each of these queries, the new approach took 5 minutes and 19 seconds total. The average response time for each query was about 116 milliseconds with the median response time less than 100 milliseconds. Table 1 shows ten of these queries, response time, and three top relevant sentences retrieved by the new system. In addition, the response time for the previous system is mentioned. Figure 3 illustrates the distribution of response time for the queries.

Bottom Line: Motivated by the recent advance in semantically enhanced information retrieval, we have developed a system, which aims to bring semantically enriched literature, Semantic Medline, to meet the information needs at point of care.This study reports our work towards operationalizing the system for real time use.We demonstrate that the migration of a relational database implementation to a NoSQL (Not only SQL) implementation significantly improves the performance and makes the use of Semantic Medline at point of care decision support possible.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN.

ABSTRACT
Scientific literature is one of the popular resources for providing decision support at point of care. It is highly desirable to bring the most relevant literature to support the evidence-based clinical decision making process. Motivated by the recent advance in semantically enhanced information retrieval, we have developed a system, which aims to bring semantically enriched literature, Semantic Medline, to meet the information needs at point of care. This study reports our work towards operationalizing the system for real time use. We demonstrate that the migration of a relational database implementation to a NoSQL (Not only SQL) implementation significantly improves the performance and makes the use of Semantic Medline at point of care decision support possible.

No MeSH data available.