Medical data mining: knowledge discovery in a clinical data warehouse
Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In thi...
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| Veröffentlicht in: | Proceedings - AMIA Annual Fall Symposium 1997, p.101-105 |
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| creator | Prather, J C Lobach, D F Goodwin, L K Hales, J W Hage, M L Hammond, W E |
| description | Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis. |
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Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. 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Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis.</description><subject>Data Interpretation, Statistical</subject><subject>Databases, Factual</subject><subject>Factor Analysis, Statistical</subject><subject>Female</subject><subject>Humans</subject><subject>Medical Records Systems, Computerized</subject><subject>Perinatal Care</subject><subject>Pregnancy - statistics & numerical data</subject><subject>Pregnancy Outcome</subject><issn>1091-8280</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkEtLAzEUhbNQaq3-BCErdwN5TiYuBCn1ARU3ug553GmjmUydmbb03ztgKbq6i_PxHc49Q1NKNC0qVpELdNn3n4SUlNBygiaaSyW1mqLFK4TobcLBDhY3Mce8usNfud0nCCvAIfa-3UF3wDFji30aiRO-tx2s220PV-i8tqmH6-OdoY_Hxfv8uVi-Pb3MH5bFhjM9FEFyySsvqaqpphUnwnktXE0AmCNQa8eYr7nwUrjKSq5KGlxdaiKCJAoqPkP3v97N1jUQPOShs8lsutjY7mBaG83_JMe1WbU7wxjngshRcHsUdO33FvrBNONASMlmGIcYpbkmSqgRvPnbdKo4Po7_AGj0afM</recordid><startdate>1997</startdate><enddate>1997</enddate><creator>Prather, J C</creator><creator>Lobach, D F</creator><creator>Goodwin, L K</creator><creator>Hales, J W</creator><creator>Hage, M L</creator><creator>Hammond, W E</creator><general>American Medical Informatics Association</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>1997</creationdate><title>Medical data mining: knowledge discovery in a clinical data warehouse</title><author>Prather, J C ; Lobach, D F ; Goodwin, L K ; Hales, J W ; Hage, M L ; Hammond, W E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p329t-d53538c517f1918304bc94bf0ee2b0ef9b22cf34c54b8a53761dbf6904d507e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Data Interpretation, Statistical</topic><topic>Databases, Factual</topic><topic>Factor Analysis, Statistical</topic><topic>Female</topic><topic>Humans</topic><topic>Medical Records Systems, Computerized</topic><topic>Perinatal Care</topic><topic>Pregnancy - statistics & numerical data</topic><topic>Pregnancy Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Prather, J C</creatorcontrib><creatorcontrib>Lobach, D F</creatorcontrib><creatorcontrib>Goodwin, L K</creatorcontrib><creatorcontrib>Hales, J W</creatorcontrib><creatorcontrib>Hage, M L</creatorcontrib><creatorcontrib>Hammond, W E</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Proceedings - AMIA Annual Fall Symposium</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Prather, J C</au><au>Lobach, D F</au><au>Goodwin, L K</au><au>Hales, J W</au><au>Hage, M L</au><au>Hammond, W E</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Medical data mining: knowledge discovery in a clinical data warehouse</atitle><jtitle>Proceedings - AMIA Annual Fall Symposium</jtitle><addtitle>Proc AMIA Annu Fall Symp</addtitle><date>1997</date><risdate>1997</risdate><spage>101</spage><epage>105</epage><pages>101-105</pages><issn>1091-8280</issn><abstract>Clinical databases have accumulated large quantities of information about patients and their medical conditions. 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| issn | 1091-8280 |
| language | eng |
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| source | MEDLINE; PubMed Central |
| subjects | Data Interpretation, Statistical Databases, Factual Factor Analysis, Statistical Female Humans Medical Records Systems, Computerized Perinatal Care Pregnancy - statistics & numerical data Pregnancy Outcome |
| title | Medical data mining: knowledge discovery in a clinical data warehouse |
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