Literature DB >> 9357597

Medical data mining: knowledge discovery in a clinical data warehouse.

J C Prather1, D F Lobach, L K Goodwin, J W Hales, M L Hage, W E Hammond.   

Abstract

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.

Entities:  

Mesh:

Year:  1997        PMID: 9357597      PMCID: PMC2233405     

Source DB:  PubMed          Journal:  Proc AMIA Annu Fall Symp        ISSN: 1091-8280


  3 in total

1.  The tertiary center and health departments in cooperation: the Duke University experience.

Authors:  M E Burkett
Journal:  J Perinat Neonatal Nurs       Date:  1989-01       Impact factor: 1.638

2.  Converting a legacy system database into relational format to enhance query efficiency.

Authors:  J C Prather; D F Lobach; J W Hales; M L Hage; S J Fehrs; W E Hammond
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1995

3.  Machine learning for an expert system to predict preterm birth risk.

Authors:  L K Woolery; J Grzymala-Busse
Journal:  J Am Med Inform Assoc       Date:  1994 Nov-Dec       Impact factor: 4.497

  3 in total
  33 in total

1.  Framework for characterizing data and identifying anomalies in health care databases.

Authors:  A M Savage
Journal:  Proc AMIA Symp       Date:  1999

2.  Extended SQL for manipulating clinical warehouse data.

Authors:  S B Johnson; D Chatziantoniou
Journal:  Proc AMIA Symp       Date:  1999

3.  How the past teaches the future: ACMI distinguished lecture.

Authors:  W E Hammond
Journal:  J Am Med Inform Assoc       Date:  2001 May-Jun       Impact factor: 4.497

4.  Metadata-driven ad hoc query of patient data: meeting the needs of clinical studies.

Authors:  Aniruddha M Deshpande; Cynthia Brandt; Prakash M Nadkarni
Journal:  J Am Med Inform Assoc       Date:  2002 Jul-Aug       Impact factor: 4.497

5.  A knowledge-anchored integrative image search and retrieval system.

Authors:  Selnur Erdal; Umit V Catalyurek; Philip R O Payne; Joel Saltz; Jyoti Kamal; Metin N Gurcan
Journal:  J Digit Imaging       Date:  2007-11-27       Impact factor: 4.056

6.  Development of a Google-based search engine for data mining radiology reports.

Authors:  Joseph P Erinjeri; Daniel Picus; Fred W Prior; David A Rubin; Paul Koppel
Journal:  J Digit Imaging       Date:  2008-04-05       Impact factor: 4.056

7.  Online practice guidelines: issues, obstacles, and future prospects.

Authors:  R D Zielstorff
Journal:  J Am Med Inform Assoc       Date:  1998 May-Jun       Impact factor: 4.497

Review 8.  The Necessity of Data Mining in Clinical Emergency Medicine; A Narrative Review of the Current Literatrue.

Authors:  Elahe Parva; Reza Boostani; Zahra Ghahramani; Shahram Paydar
Journal:  Bull Emerg Trauma       Date:  2017-04

9.  Mining medical data: a case study of endometriosis.

Authors:  Yi-Fan Wang; Ming-Yang Chang; Rui-Dong Chiang; Lain-Jinn Hwang; Cho-Ming Lee; Yi-Hsin Wang
Journal:  J Med Syst       Date:  2013-01-17       Impact factor: 4.460

10.  Exploring clinical associations using '-omics' based enrichment analyses.

Authors:  David A Hanauer; Daniel R Rhodes; Arul M Chinnaiyan
Journal:  PLoS One       Date:  2009-04-13       Impact factor: 3.240

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