Message-ID: <319834425.7412.1711615865975.JavaMail.confluence@ip-172-30-4-17.ec2.internal> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_7411_1725131018.1711615865973" ------=_Part_7411_1725131018.1711615865973 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html i2b2 Data Mart & Ontology

i2b2 Data Mart & Ontology

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This page is a brief overview of the i2b2 Data Mart and Ontology i= n relation to the Multi-fact table project. It only touches the surfaces of= the i2b2 database schema and is not meant to be a full tutorial.=20

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Data Storage

The i2b2 data is stored in one of three supported relational databases, = and always in a star schema format. A star schema contains one fact and man= y dimension tables. This statement although true is no longer completely ac= curate for the i2b2. With the release of version 1.7.09, the i2b2 server ca= n now support multiple fact tables. The standard i2b2 demo data is released= following the star schema format. However, sites have the option to use mo= re than one fact table in their own environments.

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Ontology Data

i2b2 ontology data consists of one or more metadata tables. If there is = one table, it will contain all the possible data types or categories. The o= ther option is to have one table for each data type. Examples of data types= are: diagnoses, procedures, demographics, lab tests, encounter (visits or = observations), providers, health history, transfusion data, microbiology da= ta and diverse types of genetics data.

All metadata tables must have the same basic structure. The structure of= the metadata is integral to the visualization of concepts in the i2b2 work= bench as well as for querying the data.

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Data Mart

The i2b2 data mart is a data warehouse consisting of one fact table and = several dimension tables that provide additional information about fields i= n the fact table.

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What is a fact?

In healthcare, a logical fact is an observation on a patient. It is impo= rtant to note that an observation may not represent the onset or date of th= e condition or event being described, but instead is simply a recording or = a notation of something. For example, the observation of =E2=80=98diabetes= =E2=80=99 recorded in the database as a =E2=80=98fact=E2=80=99 at a particu= lar time does not mean that the condition of diabetes began exactly at that= time, only that a diagnosis was recorded at that time (there may be many d= iagnoses of diabetes for this patient over time).

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Fact Table

The fact table contains the basic attributes about the observation, such= as the patient and provider numbers, a concept code for the concept observ= ed, a start and end date, and other parameters described in this document.&= nbsp; In the i2b2, the fact table is called observation_fact.

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3D"important" Important

In the i2b2, you are = required to have one fact table called observation_fact.
When the mult= i-fact table feature is turned off or other fact tables are not defined in = the metadata tables, the i2b2 server will continue to search the observatio= n_fact table.
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Dimension Table

Dimension tables contain further descriptive and analytical information = about attributes in the fact table. A dimension table may contain informati= on about how certain data is organized, such as a hierarchy that can be use= d to categorize or summarize the data. In the i2b2 data mart, there are sev= eral dimension tables that provide additional information about fields in t= he fact table.

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