title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ Ð¿Ð¾Ð´Ð´ÐµÑÐ¶ÐºÐ¸ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ Ð²ÑÐ°ÑÐµÐ±Ð½ÑÑ ÑÐµÑÐµÐ½Ð¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ½Ð¸Ð¼ÐºÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of a clinical decision support system using deep learning algorithms for diagnostics and patient condition forecasting based on MRI images
Creators: name: ÐÐ»Ð»Ð¸ÑÐ¸, ÐÐ°Ð½Ð¸ÑÐ»Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÑÐ¸ÑÑÐµÐ¼Ð° Ð¿Ð¾Ð´Ð´ÐµÑÐ¶ÐºÐ¸ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ Ð²ÑÐ°ÑÐµÐ±Ð½ÑÑ ÑÐµÑÐµÐ½Ð¸Ð¹
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ°
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ðµ Ð¿Ð°ÑÐ¸ÐµÐ½ÑÐ°
ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð²ÑÐºÐ¸Ðµ ÑÐ½Ð¸Ð¼ÐºÐ¸
decision support system for medical decision making
deep learning algorithms
diagnosis; prognosis
patient condition
MRI images

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description: The aim of the study is to create an effective decision support system using deep learning algorithms for diagnosis and prediction of the patients condition based on radiographic images and epidemiological data. The following objectives were accomplished: - investigation of existing technologies and solutions; - construction of predictive models for patient conditions using radiographic images and epidemiological data; - examination of potential strategies for optimizing the utilization of these technologies, with a focus on cost reduction. The relevance of the topic is underscored by the rising amounts of medical data, requiring their efficient analysis to expedite and enhance diagnostic accuracy. Additionally, the integration of deep learning algorithms facilitates the automation of medical decision-making processes, thereby contributing to an improvement in the quality of healthcare services. The sources of information comprised scientific articles and monographs about deep learning and medical diagnostics, databases containing radiographic images, and interviews with active physicians. The result of the work is the creation of a decision support system, which showcases high precision in both diagnosis and prediction of a patients condition. This system holds potential for integration into clinical practice, offering assistance to physicians during the decision-making process.