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9篇 您的检索式：作者名="Lisitsyna"

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		题名	作者	年代	出处	被引量
	1	Prevalence ofm en- tal disorders in SLE pat ient s: correlat ions with the disease activity and comorbid chronic conditions 显示文摘	Lisitsyna TA Veltishchev D Seravina OF	2009	Ter Arkh2009，81，6：	1
	2	Prevalence of mental disorders in SLE patients:correlations with the disease activity and comorbid chronic conditions 显示文摘	Lisitsyna TA Vel' tishchev DIu Seravina OF	2009	Ter Arkh2009，81，：	1
	3	Prevalence of mental disorders in SLE patients:correlations with the disease activity and comorbid chronic conditions显示文摘	Lisitsyna TA Vel'tishchev Dlu Seravina OF	2009	Ter Arkh2009，81，6：	1
	4	Prevalence of mental disorders in SLE patients: correlations with the disease activity and comorbid chronic conditions 显示文摘	Lisitsyna TA Vel'tishchev DIu Seravina OF	2009	Ter Arkh2009，81，6：	1
	5	Prevalence of mental disorders in SLE patients:correlations with the disease activity and comorbid chronic conditions显示文摘	Lisitsyna TA Veltis shchey Dlu Semvina OF	2009	Ter Arkh2009，81，6：	1
	6	Paraoxonase: biological activity and clinical implications显示文摘	Voronin MV Lisitsyna TA Durnev AD	2008	Vestn Ross Akad Med Nauk2008，，9：	1
	7	Preva-lence of mental disorders in SLE patients:correlations with the disease activity and comorbid chronic conditions显示文摘	Lisitsyna T A Vel'Tishchev D Seravina OF		0，，6：	1
	8	Perceptionof pain in rheumatoid arthritis: relation to inflammation, psychicdisorders, functional status,and quality of life 显示文摘	Lisitsyna TA Veltishchev DIu Gerasimov AN ei al	2013	Klinicheska-ia meditsina2013，91，3：	1
	9	Churn Prediction Task in MOOC显示文摘Churn prediction is a common task for machine learning applications in business.In this paper,this task is adapted for solving problem of low efficiency of massive open online courses(only 5%of all the students finish their course).The approach is presented on course“Methods and algorithms of the graph theory”held on national platform of online education in Russia.This paper includes all the steps to build an intelligent system to predict students who are active during the course,but not likely to finish it.The first part consists of constructing the right sample for prediction,EDA and choosing the most appropriate week of the course to make predictions on.The second part is about choosing the right metric and building models.Also,approach with using ensembles like stacking is proposed to increase the accuracy of predictions.As a result,a general approach to build a churn prediction model for online course is reviewed.This approach can be used for making the process of online education adaptive and intelligent for a separate student.	Lisitsyna Liubov Oreshin SA	2019	Journal of Computer Science Research2019，1，1：	0

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