Data mining and clinical data repositories: Insights from a 667,000 patient data set.
Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner, to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner approaches knowledge discovery using three unsupervised methods: CliniMiner, Predictive Analysis, and Pattern Discovery. The initial results from this study suggest that these approaches have the potential to expand research capabilities through identification of potentially novel clinical disease associations.
Mullins
Irene M
IM
Department of Public Health Sciences, University of Virginia Health System, Charlottesville, VA, USA.
Siadaty
Mir S
MS
Lyman
Jason
J
Scully
Ken
K
Garrett
Carleton T
CT
Miller
W Greg
WG
Muller
Rudy
R
Robson
Barry
B
Apte
Chid
C
Weiss
Sholom
S
Rigoutsos
Isidore
I
Platt
Daniel
D
Cohen
Simona
S
Knaus
William A
WA
D019963:Clinical Chemistry Tests; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006801:Humans; D008492:Medical Informatics Computing; D016347:Medical Records Systems, Computerized; D011237:Predictive Value of Tests