Prognostic Value of Ceramide Dynamics in Patients with Acute Coronary Syndrome.

A dynamic study of ceramide concentrations and their association with recurrent event risk could enhance our understanding of cardiovascular complications. To assess the prognostic value of ceramide concentrations (Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:1), Cer(d18:1/24:0)) and their dynamics in combination with standard clinical and laboratory parameters and therapeutic interventions in ACS patients. Among 110 ACS patients, triple blood sampling was performed for targeted lipidomic analysis using high-performance liquid chromatography-tandem mass spectrometry.

Unraveling Endometrial Cancer Survival Predictors Through Advanced Machine Learning Techniques.

This study explores endometrial cancer (EC) within the broader context of oncogynecology, focusing on 3,845 EC patients at the Almazov National Research Center. The research analyzes clinical data, employing machine learning techniques like random forest regression and decision tree analysis. Key findings include age-dependent impacts on EC outcomes, unexpected correlations between dietary habits and recurrence risk (e.

Development of a Method for Automatic Matching of Unstructured Medical Data to ICD-10 Codes.

Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complaints) to ICD-10 codes.

Time Series Forecasting of Cardiovascular Mortality: Machine Learning Based on State Economic and Local Medical Data.

This study focuses on the complex interplay of healthcare, economic factors, and population dynamics, addressing a research gap in regional-level models that integrate diverse features within a temporal framework. Our primary objective is to develop an advanced temporal model for predicting cardiovascular mortality in Russian regions by integrating global and local healthcare features with economic and population dynamics. Utilizing a dataset from the Almazov Center's Department of Mortality Performance Monitoring, covering 94 regions and 752 records from January 1, 2015, to December 31, 2023, our analysis incorporates key parameters such as angioplasty procedures, population morbidity rates, Ischemic Heart Disease (IHD) and Cardiovascular Diseases (CVD) monitoring, and demographic data.

Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods.

Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was to analyze the frequency of AS in a population of cardiovascular patients using echocardiography (ECHO) and to identify clinical factors and features associated with these patient groups.