Authors:
Roman Suvorov
1
;
Ivan Smirnov
2
;
Konstantin Popov
3
;
Nikolay Yarygin
4
and
Konstantin Yarygin
5
Affiliations:
1
Institute for Systems Analysis of Russian Academy of Sciences, Russian Federation
;
2
Institute of Systems Analysis of Russian Academy of Sciences, Russian Federation
;
3
Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Russian Federation
;
4
State University of Medicine and Dentistry, Russian Federation
;
5
Russian Academy of Medical Sciences, Russian Federation
Keyword(s):
Clinical Trials, Meta Analysis, Information Retrieval, Natural Language Processing, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Active Learning
;
Applications
;
Artificial Intelligence
;
Classification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
On-Line Learning
;
Pattern Recognition
;
Symbolic Systems
;
Theory and Methods
Abstract:
To estimate patients risks and make clinical decisions, evidence based medicine (EBM) relies upon the results
of reproducible trials and experiments supported by accurate mathematical methods. Experimental and clinical
evidence is crucial, but laboratory testing and especially clinical trials are expensive and time-consuming.
On the other hand, a new medical product to be evaluated may be similar to one or many already tested.
Results of the studies hitherto performed with similar products may be a useful tool to determine the extent
of further pre-clinical and clinical testing. This paper suggests a workflow design aimed to support such
an approach including methods for information collection, assessment of research reliability, extraction of
structured information about trials and meta-analysis. Additionally, the paper contains a discussion of the
issues emering during development of an integrated software system that implements the proposed workflow.