Creation of a Medical Decision Support System Using Evidence-Based Medicine

This article presents a new study related to the creation of a medical decision support system with an intellectual analysis of scientific data (texts of medical care standards, clinical guidelines, instructions for the use of medicines, scientific publications of evidence-based medicine). Such a system is designed to provide the possibility of making medical decisions in pharmacotherapy, taking into account personalized medical data due to the optimal prescription of medicines and the use of medical technologies, reducing the frequency of undesirable reactions while using two or more drugs for different indications. The technical goal of the study is to create an intelligent automated information system to support the adoption of medical decisions and its implementation in clinical practice. This work was supported by a grant from the Ministry of Education and Science of the Russian Federation, a unique project identifier RFMEFI60819X0278.

[1]  Anthony F. J. van Raan,et al.  Exploring the Relationship between the Engineering and Physical Sciences and the Health and Life Sciences by Advanced Bibliometric Methods , 2014, PloS one.

[2]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[4]  Raja Mazumder,et al.  DiMeX: A Text Mining System for Mutation-Disease Association Extraction , 2016, PloS one.

[5]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[6]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[7]  Zhiyong Lu,et al.  Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health. , 2016, Advances in experimental medicine and biology.

[8]  D. Rebholz-Schuhmann,et al.  Text-mining solutions for biomedical research: enabling integrative biology , 2012, Nature Reviews Genetics.

[9]  Polina Mihova,et al.  Attitudes to Telemedicine, and Willingness to Use in Young People , 2019, KES-IDT.

[10]  Ralph Grishman,et al.  Joint Event Extraction via Recurrent Neural Networks , 2016, NAACL.

[11]  Chaomei Chen,et al.  Visualizing knowledge domains , 2005, Annu. Rev. Inf. Sci. Technol..

[12]  Patrick Ruch,et al.  Text Mining to Support Gene Ontology Curation and Vice Versa. , 2017, Methods in molecular biology.

[13]  Kurt Hornik,et al.  Text Mining Infrastructure in R , 2008 .

[14]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[15]  Alan L. Porter,et al.  Capturing new developments in an emerging technology: an updated search strategy for identifying nanotechnology research outputs , 2013, Scientometrics.