scholarly journals e-Health: Data Integration, Data Mining, and Knowledge Management in Health Informatics

2010 ◽  
pp. 1-1
Author(s):  
Yanchun Zhang
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Data Integration ◽  
Health Informatics ◽  
Health Data ◽  
Integration Data

scholarly journals ANALYSIS OF VARIOUS CLUSTERING ALGORITHMS OF DATA MINING ON HEALTH INFORMATICS

2016 ◽  
pp. 75-79
Author(s):  
PANKAJ SAXENA ◽  
SUSHMA LEHRI
Keyword(s):  
Data Mining ◽  
Health Informatics ◽  
Clustering Algorithms ◽  
Health Data ◽  
Machine Leaning ◽  
Medical Documents ◽  
Different Dimensions ◽  
Analytical Tools ◽  
Mining Tools ◽  
Analyze Data

There are large quantities of information about patients and their medical conditions. The discovery of trends and patterns hidden within the data could significantly enhance understanding of disease and medicine progression and management by evaluating stored medical documents. Methods are needed to facilitate discovering the trends and patterns within such large quantities of medical documents. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Weka is a data mining tools. It contains many machine leaning algorithms. It provides the facility to classify our data through various algorithms. In this paper we are studying the various clustering algorithms. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Our main aim to show the comparison of different clustering algorithms of Data Mining and find out which algorithm will be most suitable for the users working on health data.

scholarly journals Special Issue on Data Mining in Health Informatics

2018 ◽  
Vol 2 (4) ◽  
pp. 367-369
Author(s):  
Xia Hu ◽  
Gregor Štiglic ◽  
Fei Wang
Keyword(s):  
Data Mining ◽  
Health Informatics ◽  
Special Issue

Regulatory Challenges of Data Mining Practices: The Case of the Never-ending Lifecycles of ‘Health Data’

2018 ◽  
Vol 25 (3) ◽  
pp. 284-307
Author(s):  
Giovanni Comandè ◽  
Giulia Schneider
Keyword(s):  
Data Mining ◽  
Data Protection ◽  
Personal Data ◽  
Health Data ◽  
General Level ◽  
General Data Protection Regulation ◽  
The Face ◽  
General Data ◽  
Level Of Analysis

Abstract Health data are the most special of the ‘special categories’ of data under Art. 9 of the General Data Protection Regulation (GDPR). The same Art. 9 GDPR prohibits, with broad exceptions, the processing of ‘data concerning health’. Our thesis is that, through data mining technologies, health data have progressively undergone a process of distancing from the healthcare sphere as far as the generation, the processing and the uses are concerned. The case study aims thus to test the endurance of the ‘special category’ of health data in the face of data mining technologies and the never-ending lifecycles of health data they feed. At a more general level of analysis, the case of health data shows that data mining techniques challenge core data protection notions, such as the distinction between sensitive and non-sensitive personal data, requiring a shift in terms of systemic perspectives that the GDPR only partly addresses.

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