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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.


Architecture comparison of two implementations.
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f1-2092053: Architecture comparison of two implementations.

Mentions: There are multiple steps in the previous system[8] as shown in the top part of Figure 1. The first step, query processing, involved: tokenization, lexical normalization, UMLS Metathesaurus look-up, and concept screening and Medical Subject Heading (MeSH) conversion. After processing and expanding user’s query, the system used NCBI E-utils to retrieve relevant Medline abstracts. E-utils returned PubMed identifier (PMID) and meta-data of relevant abstracts. In the next step, the system queried SemMedDB to retrieve sentences that appeared in these abstracts containing at least one semantic predication. With respect to the SemMedDB design, the system needed to join two tables that one of them has more than 17 million rows and the other one, more than 143 million rows. Then the system ranked the retrieved sentences. In the new system, we integrated all needed resources into one place and instead of using a relational database, one of the common search engines, ElasticSearch (ES), is utilized to store and search the data. ES is a search engine built upon Apache Lucene and supports distributed implementation. To implement the new system, we first downloaded needed resources including Metadata for Medline abstracts (retrieved from PubMed), predication information from SemMedDB, and SCImago journal and country ranking information [20]. After downloading the resources, we indexed and stored them in ES. As ES is a document-based search engine, we first formed documents and then indexed them. Unlike RDB, which requires a carefully defined schema, a document in ES contains a record or tuple without a predefined schema. A document in ES is equivalent to a row in relational database. Each document in our index contains: sentence, abstract’s metadata, and journal’s rank information. Figure 2 illustrates the original source of each field in ES’s document. After building the index, we were able to query the index and retrieve relevant sentences to user query. As mentioned earlier, the query could be one word, multiple words, or sentence. The system followed the same method, we used in the previous system[8], to process and expand the query. ES retrieved and ranked relevant sentences. As we integrated Medline abstracts metadata and SemMedDB data, the system did not need to query two resources. Like the previous system, we uses publication type and journal score to rank the retrieved sentences (more detail about the ranking method in[9], [10]). Integrating the ranking and searching is one of the advantages of using ES. The bottom part of Figure 1 illustrates the architecture of our new implementation.


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)

Architecture comparison of two implementations.
© Copyright Policy
Related In: Results  -  Collection

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

f1-2092053: Architecture comparison of two implementations.
Mentions: There are multiple steps in the previous system[8] as shown in the top part of Figure 1. The first step, query processing, involved: tokenization, lexical normalization, UMLS Metathesaurus look-up, and concept screening and Medical Subject Heading (MeSH) conversion. After processing and expanding user’s query, the system used NCBI E-utils to retrieve relevant Medline abstracts. E-utils returned PubMed identifier (PMID) and meta-data of relevant abstracts. In the next step, the system queried SemMedDB to retrieve sentences that appeared in these abstracts containing at least one semantic predication. With respect to the SemMedDB design, the system needed to join two tables that one of them has more than 17 million rows and the other one, more than 143 million rows. Then the system ranked the retrieved sentences. In the new system, we integrated all needed resources into one place and instead of using a relational database, one of the common search engines, ElasticSearch (ES), is utilized to store and search the data. ES is a search engine built upon Apache Lucene and supports distributed implementation. To implement the new system, we first downloaded needed resources including Metadata for Medline abstracts (retrieved from PubMed), predication information from SemMedDB, and SCImago journal and country ranking information [20]. After downloading the resources, we indexed and stored them in ES. As ES is a document-based search engine, we first formed documents and then indexed them. Unlike RDB, which requires a carefully defined schema, a document in ES contains a record or tuple without a predefined schema. A document in ES is equivalent to a row in relational database. Each document in our index contains: sentence, abstract’s metadata, and journal’s rank information. Figure 2 illustrates the original source of each field in ES’s document. After building the index, we were able to query the index and retrieve relevant sentences to user query. As mentioned earlier, the query could be one word, multiple words, or sentence. The system followed the same method, we used in the previous system[8], to process and expand the query. ES retrieved and ranked relevant sentences. As we integrated Medline abstracts metadata and SemMedDB data, the system did not need to query two resources. Like the previous system, we uses publication type and journal score to rank the retrieved sentences (more detail about the ranking method in[9], [10]). Integrating the ranking and searching is one of the advantages of using ES. The bottom part of Figure 1 illustrates the architecture of our new implementation.

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.