CIOTechOutlook >> Magazine >> December - 2014 issue

Proliferation in use of intelligent systems for large-scale analysis of biomedical data

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Volumes of information and data stored in medical databases require the use of specialized and intelligent tools for storing and accessing of data and data analysis. Developing intelligent tools in order to extract information that supports decision- making has been imperative in knowledge discovery, information retrieval, pattern recognition and databases. Today, artificial intelligence techniques have been used for management of patient records, medical image analysis, surgical procedures, the analysis of data in identifying risk factors, genetic sequence analysis, protein structure representation, the development of automated data analysis tools and much more. Intelligent data analysis takes in statistical, pattern recognition, machine learning, data abstraction and visualization tools to support the analysis of data and discovery of insights that are ingrained within the data. Intelligent data analysis bridges the gap between data gathering and decision making in order to enable the physician to perform his task more efficiently.

Intelligent data analysis methods have been applied to extraction and mining of data. Data abstraction is related with the intelligent interpretation of patient data in a context-sensitive manner and the presentation of such interpretations in a visual or symbolic form. Furthermore, data mining is related to the discovery of medical insights from data aimed to support diagnostic, screening and monitoring tasks of the patient. Intelligent data analysis is gaining traction in the healthcare sector and aims to bridge the gap of data gathering and interpretation to form insights.



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