Sleep-Disordered Breathing and Prognosis after Ischemic Stroke: It Is Not Apnea-Hypopnea Index That Matters.

Background: Sleep-disordered breathing (SDB) is highly prevalent after stroke and is considered to be a risk factor for poor post-stroke outcomes. The aim of this observational study was to evaluate the effect of nocturnal respiratory-related indices based on nocturnal respiratory polygraphy on clinical outcomes (including mortality and non-fatal events) in patients with ischemic stroke.

Methods: A total of 328 consecutive patients (181 (55%) males, mean age 65.

Application of Machine Learning Methods for Epilepsy Risk Ranking in Patients with Hematopoietic Malignancies Using.

Machine learning methods to predict the risk of epilepsy, including vascular epilepsy, in oncohematological patients are currently considered promising. These methods are used in research to predict pharmacoresistant epilepsy and surgical treatment outcomes in order to determine the epileptogenic zone and functional neural systems in patients with epilepsy, as well as to develop new approaches to classification and perform other tasks. This paper presents the results of applying machine learning to analyzing data and developing diagnostic models of epilepsy in oncohematological and cardiovascular patients.

Identification of Diabetes Risk Factors in Chronic Cardiovascular Patients.

Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS, can reach up to AUC 65.8 - 84.7 % for the conditional diagnosis of DPN in primary care.

Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study.

Background: Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one.

Risk Factors for Chronic Diabetes Patients.

Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS), can reach up to AUC 65.8-84.7 % for the conditional diagnosis of DPN in primary care.