Using the method of artificial neural networks for integration into the decision support system as a tool for optimizing outpatient management of patients with chronic obstructive pulmonary disease

Autor: T. V. Tayutina, S. V. Shlyk, A. S. Vodopyanov, T. M. Kazaryan

Publikováno v: Медицинский вестник Юга России, Vol 15, Iss 1, Pp 126-140 (2024)

Objective: to evaluate the possibility of using artificial neural networks for integration into the medical decision support system as an optimization of outpatient management of patients with COPD.Materials and methods: a dynamic followup of 150 pat

Externí odkaz: https://doaj.org/article/44bd48aad67a4e66989d53009f0ef53b

Areas for Artificial Intelligence Implementation in Kuzbass Healthcare: Sociological Aspects

Autor: Olga V. Golovko, Evgeny I. Kharlampenkov

Publikováno v: Вестник Кемеровского государственного университета. Серия: политические, социологические и экономические науки, Vol 8, Iss 1, Pp 40-49 (2023)

The article analyzes the structure of morbidity in the region and identifies the main directions for artificial intelligence implementation in Russia. In order to identify the attitude of clinical physicians towards the artificial intelligence produc

Externí odkaz: https://doaj.org/article/c460cdcf8af2431880cc7c5252e787d9

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

Autor: Abas Hasanovich Lampezhev, Elena Yur`evna Linskaya, Aslan Adal`bievich Tatarkanov, Islam Alexandrovich Alexandrov

Publikováno v: Emerging Science Journal, Vol 5, Iss 5, Pp 688-699 (2021)

This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical dat

Externí odkaz: https://doaj.org/article/f2e1d755ab9e4907bb3ff3bd864cfab1

A deep learning pipeline for automatized assessment of spinal MRI

Autor: Irina Balzer, Malin Mühlemann, Moritz Jokeit, Ishaan Singh Rawal, Jess G. Snedeker, Mazda Farshad, Jonas Widmer

Publikováno v: Computer Methods and Programs in Biomedicine Update, Vol 2, Iss , Pp 100081- (2022)

Background: This work evaluates the feasibility, development, and validation of a machine learning pipeline that includes all tasks from MRI input to the segmentation and grading of the intervertebral discs in the lumbar spine, offering multiple diff

Externí odkaz: https://doaj.org/article/0f069844c7f34421b145547432980226