Knowledge Discovery in Clinical Data

Author(s):  
Aryya Gangopadhyay ◽  
Rose Yesha ◽  
Eliot Siegel
2018 ◽  
Author(s):  
Martin G. Seneviratne ◽  
Michael G. Kahn ◽  
Tina Hernandez-Boussard

Author(s):  
Francesca Frexia ◽  
Cecilia Mascia ◽  
Luca Lianas ◽  
Giovanni Delussu ◽  
Alessandro Sulis ◽  
...  

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs).


The information about patients and their medical condition are stored in a large clinical database. In this data the different patterns and relationship give new medicinal learning. To find this hidden knowledge several methods and techniques are developed. The research proposed a data mining technique in huge clinical database for searching the relationships. This data mining technique is known as Knowledge Discovery in Databases. This research defines different methods and process of data mining in clinical database.


1992 ◽  
Vol 670 (1 Extended Clin) ◽  
pp. 146-154
Author(s):  
JOHN M. LONG ◽  
JAMES R. SLAGLE ◽  
THE POSCH GROUP

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