Information and Analytical Support for Biomedical Research in the Field of the Cardiovascular Disease Risk Prediction
- DOI
- 10.2991/aisr.k.201029.017How to use a DOI?
- Keywords
- information and analytical support of scientific research, Data mining, information extraction, machine learning, electronic medical records, digital patient phenotype, medical text processing, personified prediction, cardiovascular diseases
- Abstract
The aim of the article is to study Data mining methods to predict the cardiovascular disease risks’ level, based on data from medical information systems. We propose a new approach to the development of information and analytical support for biomedical research. Based on the proposed approach, we have developed the special methods, technologies and services to extract and anonymize valid problem-oriented information from unstructured electronic medical records. Created data store contains more than 70,000 records of electronic medical records and provides the researcher anonymized “smart” information in accordance with the possible scenario of its use. The developed services for visualizing the values of objective indicators allow us to determine the optimal data structure for the diagnosis of a specific group of diseases. This is shown by the example of risk testers of cardiovascular diseases. Based on the selected indicators, models for personified prediction of the cardiovascular disease risks’ level were developed and tested)/
- Copyright
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Alexander A. Zakharov AU - Alexander A. Kotelnikov AU - Alexander P. Potapov AU - Dmitry V. Panfilenko AU - Pavel Y. Gayduk PY - 2020 DA - 2020/11/10 TI - Information and Analytical Support for Biomedical Research in the Field of the Cardiovascular Disease Risk Prediction BT - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020) PB - Atlantis Press SP - 82 EP - 88 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.201029.017 DO - 10.2991/aisr.k.201029.017 ID - Zakharov2020 ER -