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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Предикторы годовой выживаемости после ишемического инсульта

Неврология, нейропсихиатрия, психосоматика. 2025. Т. 17. № 5. С. 48–54.
Kulikova S., Polyakova I., Kuzmicheva E., Кулеш А. А., Мехряков С. А., Кулеш А. М., Крапивин С. В., Каракулова Ю. В.

Predicting the outcome of ischaemic stroke (IS) is a complex task, as mortality and disability depend on many factors, including age, gender, type and severity of stroke, and comorbidities. Survival rates also vary between countries depending on genetic characteristics and differences in the organisation of healthcare systems.
Objective: to search for predictors of one-year survival after IS in a sample of patients from the Perm region.
Material and methods. The study included 254 patients who had suffered an IS. Seventy-five parameters obtained during routine clinical examination were analysed, including information on the subtype and severity of the stroke, the size and location of the lesion, neurological disorders, comorbidities, and other factors. Relevant features were selected using the WEKA programme, and the selected features were used in a predictive model based on logistic regression.
Results. The following factors have been identified as significant predictors of annual survival in patients who have undergone IS (the sign of the coefficient reflects the relative contribution of the factor to the model and its positive or negative effect): age (-0.02), degree of neurological deficit on the NIHSS scale at discharge (-0.06), haemoglobin level (0.01), infarction in the anterior choroidal artery basin (0.74), recurrent stroke within the following year (-0.02) and cardioembolic stroke subtype (-0.32). The accuracy of the logistic model was 84% with 10-fold cross-validation.
Conclusion. In the model predicting one-year survival after IS, other factors have been identified in addition to age, which is usually associated with a less favourable prognosis. Further multicentre studies are needed to confirm the reliability of the proposed model

Research target: Medical and Health Sciences
Language: Russian
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Keywords: ischemic strokeишемический инсультИсходpredictive modelsМРТпредиктивная модельoutcome predictionBrain MRI
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