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Matchings and Decision Trees for Determining Optimal Therapy
P. 101–110.
Language:
English
Publication based on the results of:
In book
Vol. 439. , Berlin: Springer, 2014.
Pshichenko D., International Journal of Humanities and Natural Sciences 2024 Vol. 8-3(95) P. 180–185
This study explores the application of artificial intelligence (AI) and machine learning (ML) models for big data analysis in project management. By leveraging specific ML algorithms such as decision trees, random forests, support vector machines, neural networks, kmeans clustering, gradient boosting, and natural language processing, project management practices are significantly enhanced. These technologies improve decision-making, ...
Added: March 10, 2025
Alexander Kirdeev, Konstantin Burkin, Vorobev A. et al., Frontiers in Medicine 2024 Vol. 11 Article 1452239
Background: The development of prognostic models for the identification of high-risk myocardial infarction (MI) patients is a crucial step toward personalized medicine. Genetic factors are known to be associated with an increased risk of cardiovascular diseases; however, little is known about whether they can be used to predict major adverse cardiac events (MACEs) for MI patients. ...
Added: November 13, 2024
Muratova A., Ignatov D. I., Mitrofanova E., , in: Recent Trends in Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020 Revised Supplementary ProceedingsVol. 12602.: Springer, 2021. P. 297–299.
This is the extended abstract of a case study on demographic sequences analysis by machine learning and data mining methods. ...
Added: November 1, 2022
Золотенкова Г. В., Rogachev A., Пиголкин Ю. И. et al., Современные технологии в медицине 2022 Т. 14 № 1 С. 15–24
The aim of the study was to assess the capabilities of age determination (age group) at death using classification techniques by histomorphometric characteristics of osseous and cartilaginous tissue aging.
Materials and Methods. The study material was a database containing the findings of morphometric researches of osseous and cartilaginous tissue histologic specimens from 294 categorized male corpses ...
Added: May 25, 2022
Dudyrev E., Kuznetsov S., , in: Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings.: Springer, 2021. Ch. 16 P. 252–260.
Added: September 28, 2021
Tyuryumina E., Neznanov A., Turumin J. L., , in: Proceedings of the AMIA 2020 Annual Symposium.: United States of America: AMIA, 2020. P. 1653–1654.
Previously, a mathematical model of primary tumor (PT) growth and secondary distant metastasis (sdMTS) growth in breast cancer (BC) (CoMPaS), considering the TNM classification, was presented. Nowadays, the updated model CoMPaS and the corresponding software tool can help to optimize the process of detecting the different diagnostic periods for sdMTSs in BC patients with different ...
Added: December 17, 2020
United States of America: AMIA, 2020.
Added: November 19, 2020
Springer, 2020.
This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.*
For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The ...
Added: November 10, 2020
Springer, 2021.
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, ...
Added: October 16, 2020
Kuralenok I., Ershov V., Лабутин И. Н., , in: Advances in Neural Information Processing Systems 32 (NeurIPS 2019).: [б.и.], 2019. P. 1–10.
In this work, we introduce a new decision tree ensemble representation framework: instead of using a graph model we transform each tree into a well-known polynomial form. We apply the new representation to three tasks: theoretical analysis, model reduction, and interpretation. The polynomial form of a tree ensemble allows a straightforward interpretation of the original ...
Added: December 27, 2019
Suleimanova A., Социология: методология, методы, математическое моделирование 2020 Т. 0 № 50-51 С. 63–96
Decision trees are a method of classification and prediction, widely applied in sociological research. It is unchangingly popular due to its flexibility and simplicity of interpretation. Choosing the most appropriate decision tree algorithm is not an easy task for several reasons: (a) there is already over a hundred algorithms with different strengths, weaknesses and logic ...
Added: October 31, 2019
Cham: Springer, 2019.
This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The 46 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data ...
Added: October 17, 2019
Bogdanov M., Lebedev D., В кн.: Вестник Российского мониторинга экономического положения и здоровья населения НИУ ВШЭ (RLMS‑HSE)Вып. 9.: М.: Издательский дом НИУ ВШЭ, 2019. С. 157–174.
The article explores how socio-demographic characteristics of Russian children and teenagers affect their academic performance at school. As the RLMS-HSE data for 2013–2017 shows, higher academic attainments are positively associated with gender, the level of parental education, class size, extracurricular and leisure activities. Meanwhile, the type of school, place of residence, and per capita household ...
Added: October 6, 2019
Makushina E., Шихлярова И. А., Финансы и кредит 2018 Т. 24 № 1 С. 95–110
The main goal of the article is to create a model for predicting a bankruptcy of Russian non-financial companies one year in advance with a minimum accuracy of 80% based on the most significant financial and non-financial variables. To obtain this goal the following tasks were issued: legal and economic approaches to determine the bankruptcy ...
Added: July 15, 2018
EasyChair, 2018.
This volume contains proceedings of the first Workshop on Data Analysis in Medicine held in May 2017 at the National Research University Higher School of Economics, Moscow. The volume contains one invited paper by Dr. Svetla Boytcheva, 6 regular contributions and 2 project proposals, carefully selected and reviewed by at least two reviewers from the ...
Added: June 8, 2018
Anton Kocheturov, Pardalos P. M., Karakitsiou A., Annals of Operations Research 2019 Vol. 276 No. 1-2 P. 5–34
This survey paper attempts to cover a broad range of topics related to computational
biomedicine. The field has been attracting great attention due to a number of benefits it can
provide the society with. New technological and theoretical advances have made it possible
to progress considerably. Traditionally, problems emerging in this field are challenging from
many perspectives. In this ...
Added: May 22, 2018
Petrov D., Dodonova Y., Zhukov L. E., , in: "Информационные технологии и системы 2015".: St. Petersburg: Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1–15.
We study di erences in structural connectomes between typically developing and autism spectrum disorders individuals with machine learning techniques using connection weights and network metrics as features. We build linear SVM classi er with accuracy score 0:64 and report 16 features (seven connection weights and nine network node centralities) best distinguishing these two groups. ...
Added: March 5, 2017
Dodonova Y., Belyaev M., Tkachev A. et al., , in: Proceedings of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention, October 17-21, 2016, Athens, Greece, Springer.: Athens: Springer, 2016. Ch. 5 P. 1–10.
In this paper, we tackle a problem of predicting phenotypes from structural connectomes. We propose that normalized Laplacian spectra can capture structural properties of brain networks, and hence graph spectral distributions are useful for a task of connectome-based classi cation. We introduce a kernel that is based on earth mover's distance (EMD) between spectral distributions of ...
Added: March 5, 2017
Athens: Springer, 2016.
MICCAI 2016, the 19th International Conference on Medical Image Computing and Computer Assisted Intervention, will be held from October 17th to 21st, 2016 in Athens, Greece. MICCAI 2016 is organized in collaboration with Bogazici, Sabanci, and Istanbul Technical Universities.
The annual MICCAI conference attracts world leading biomedical scientists, engineers, and clinicians from a wide range of ...
Added: March 5, 2017
Muratova A., Gizdatullin D., Ignatov D. I. et al., В кн.: Социология и общество: социальное неравенство и социальная справедливость (Екатеринбург , 19-21 октября 2016 года). Материалы V Всероссийского социологического конгресса.: М.: Российское общество социологов, 2016. С. 9601–9615.
In this paper, we summarize the results of recent studies on the application of pattern mining and machine learning to the analysis of demographic sequences. The main goal is the demonstration of demographers’ needs, including next-event prediction and the extraction of interesting patterns from substantial datasets of demographic data, which cannot be handled by conventional ...
Added: November 24, 2016
Ломотин К. Е., Козлова Е. С., Колесниченко А. Л. et al., В кн.: Инновационные, информационные и коммуникационные технологии: сборник трудов XIII Международной научно-практической конференции.: М.: Ассоциация выпускников и сотрудников ВВИА им. проф. Жуковского, 2016. С. 92–95.
In the article an efficiency of the modern classification instruments usage for the task of scientific articles texts rubrication according to the UDC classifier is analyzed. The following means are explored: artificial neural networks, cosine similarity, naive Bayesian classifier, decision trees and random forest. ...
Added: October 29, 2016
Kashnitsky Y., Kuznetsov S., , in: Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2016).: M.: [б.и.], 2016. P. 105–112.
Decision tree learning is one of the most popular classifica- tion techniques. However, by its nature it is a greedy approach to finding a classification hypothesis that optimizes some information-based crite- rion. It is very fast but may lead to finding suboptimal classification hy- potheses. Moreover, in spite of decision trees being easily interpretable, ensembles ...
Added: October 6, 2016