RESEARCHER PROFILE

B. I. Geltser

Vladivostok State University

Artificial Intelligence in HealthcareChronic Obstructive Pulmonary Disease (COPD) ResearchAcute Myocardial Infarction ResearchAsthma and respiratory diseasesBone health and osteoporosis research

B. I. Geltser works in Artificial Intelligence in Healthcare, Chronic Obstructive Pulmonary Disease (COPD) Research, Acute Myocardial Infarction Research, Asthma and respiratory diseases, Bone health and osteoporosis research. Recorded institutional associations include Far Eastern Federal University, Vladivostok State University.

218Publications

399Citations

9H-index

ORCID

Inclusion identifies researchers whose recorded research contributions may inform their university’s understanding of its research community. It does not rank individual research quality or establish current employment.

Selected research works

Up to 100 highly cited works; DOI links open the original research. Counts reflect the collected record, not real-time citation totals.

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B. I. Geltser

Vladivostok State University

Selected research works

Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions

The optimal duration of alarm therapy use in children with primary monosymptomatic nocturnal enuresis

Respiratory muscles dysfunction and respiratory diseases

Chronic obstructive pulmonary disease and cerebrovascular diseases: functional and clinical aspect of comorbidity

Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

Parameters of complete blood count, lipid profile and their ratios in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome

Machine learning in predicting immediate and long-term outcomes of myocardial revascularization: a systematic review

Endothelial dysfunction of cerebral and major arteries during chronic obstructive disease

Phenotyping of risk factors and prediction of inhospital mortality in patients with coronary artery disease after coronary artery bypass grafting based on explainable artificial intelligence methods

Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease

Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome

Arterial hypertension and breast cancer in women: mechanisms of comorbidity and iatrogenic disease

Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Сomorbidity of coronary artery disease and its significance in predicting the results of coronary artery bypass grafting

Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review)

FUCOIDAN FROM BROWN ALGAE FUCUS EVANESCENS: NEW PERSPECTIVES IN THE TREATMENT OF ATHEROSCLEROSIS

Comparative analysis of the strength of the respiratory muscles in community-acquired pneumonia with different severity of endogenous intoxication

Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome

Peculiarities of vasomotor reaction of cerebral vessels in arterial hypertension

Heat shock proteins - participants in osteoarthrosis pathogenesis

Assessment of Cardiac Vegetative Control during Acute Graduated Exogenous Normobaric Hypoxia in Rats

FEATURES OF CYTOKINE PROFILE IN PATIENTS WITH BRONCHIAL ASTHMA COMBINED WITH OBESITY

Assessment of indicators of pulmonary volemia and systemic inflammatory response in patients with comorbidity of chronic obstructive pulmonary disease and ischemic heart disease after coronary artery bypass grafting

Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai

Machine Learning Efficiency in Predicting Obstructive Coronary Artery Disease in Patients with Non-ST Elevation Acute Coronary Syndrome in the First Hours of Admission