A Machine Learning Approach for dentifying Disease-Treatment Relations in Short Texts
A
Machine Learning Approach for dentifying
Disease-Treatment Relations in
Short Texts
Abstract:
The
Machine Learning (ML) field has gained its momentum in almost any domain of
research and just recently has become a reliable tool in the medical domain. The
empirical domain of automatic learning is used in tasks such as medical
decision support, medical imaging, protein-protein interaction, extraction of
medical knowledge, and for overall patient management care. ML is envisioned as
a tool by which computer-based systems can be integrated in the healthcare
field in order to get a better, more efficient medical care. This paper
describes a ML-based methodology for building an application that is capable of
identifying and disseminating
healthcare
information. It extracts sentences from published medical papers that mention
diseases and treatments, and identifies semantic relations that exist between
diseases and treatments. Our evaluation results for these tasks show that the
proposed methodology obtains reliable outcomes that could be integrated in an
application to be used in the medical care domain. The potential value of this
paper stands in the ML settings that we propose and in the fact that we
outperform previous results on the same data set.
KEYWORDS:
Generic Technology Keywords: Database,
User Interface, Programming
Specific Technology Keywords: C#.Net, MS
SqlServer-08
Project Keywords: Presentation, Business Object, Data Access Layer
SDLC Keywords: Analysis, Design, Code, Testing, Implementation, Maintenance
SYSTEM
CONFIGURATION
HARDWARE
CONFIGURATION
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S.NO
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HARDWARE
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CONFIGURATIONS
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1
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Operating System
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Windows 2000 & XP
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2
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RAM
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1GB
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3
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Processor (with Speed)
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Intel
Pentium IV (3.0 GHz) and Upwards
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4
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Hard Disk Size
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40 GB and above
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5
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Monitor
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15’ CRT
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SOFTWARE
CONFIGURATION
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S.NO
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SOFTWARE
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CONFIGURATIONS
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1
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Platform
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Microsoft Visual Studio
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2
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Framework
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.Net Framework 4.0
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3
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Language
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C#.Net
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4
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Front End
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Windows application
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5
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Back End
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SQL Server 2008
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Categories:
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