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School of Data Analysis and Artificial Intelligence
Faculty of Computer Science
School of Data Analysis and Artificial Intelligence
Contacts

Pokrovsky boulevard, 11, room S938, Moscow, Russia, 109028

Phone: +7 (495) 772-95-90*27319

Administrations
About the School

The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects.

Publications
Article
Efficient indexing of peptides for database search using Tide

Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.

Journal of Proteome Research. 2023. Vol. 22. No. 2. P. 577-584.

Article
Generalized relational tensors for chaotic time series

Vasilii A. Gromov, Yury N. Beschastnov, Korney K. Tomashchuk.

PeerJ Computer Science. 2023. Vol. 9. No.  .

Article
Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets

Makhalova T., Kuznetsov S., Napoli A.

Data Mining and Knowledge Discovery. 2022. P. 108-145.

Article
Human knowledge models: Learning applied knowledge from the data

Dudyrev E., Semenkov Ilia, Kuznetsov S. et al.

Plos One. 2022. Vol. 17. No. 10.

Book chapter
Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion

Zhirayr Hayrapetyan, Nascimento S., Trevor F. et al.

In bk.: Information Systems and Technologies: WorldCIST 2022, Volume 2. Iss. 469. Springer, 2022. P. 141-147.

Book chapter
Ontology-Controlled Automated Cumulative Scaffolding for Personalized Adaptive Learning

Dudyrev F., Neznanov A., Anisimova K.

In bk.: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium -23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Springer, 2022. P. 436-439.

Book chapter
Triclustering in Big Data Setting

Egurnov D., Точилкин Д. С., Ignatov D. I.

In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.

Article
Triclusters of Close Values for the Analysis of 3D Data

Egurnov D., Ignatov D. I.

Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.

Article
Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Kudriavtseva P., Kashkinov M., Kertész-Farkas A.

Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708-4717.

Article
Language models for some extensions of the Lambek calculus

Kanovich M., Kuznetsov S., Scedrov A.

Information and Computation. 2022. Vol. 287.

All publications

Big Data and Machine Learning in Healthcare

2023/2024
Academic Year
ENG
Instruction in English
6
ECTS credits
Delivered by:
School of Data Analysis and Artificial Intelligence
Type:
Elective course
When:
2 year, 1, 2 module

Instructor

Pianykh, Oleg

Pianykh, Oleg

Course Syllabus

Full SyllabusAsk Question

Abstract

Medical Informatics (MI) is a new, exponentially-growing field, where information sciences meet modern clinical applications. The main goal of this class in to introduce HSE students to the broad spectrum of MI problems and applications, and to provide the students with the skills necessary for conduction professional MI work.
Learning Objectives

Learning Objectives

  • To develop fundamental knowledge of concepts underlying medical informatics projects.
  • To develop practical skills needed in modern digital medicine.
  • To explain how math and information sciences can contribute to building better healthcare.
  • To give a hands-on experience with real-world medical data analysis.
  • To develop applied experience with medical software, programming, applications and processes.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students are fluent in clinical data acquisition, processing and management, in the areas outlined in the schedule.
  • Students know the basic concepts of MI.
Course Contents

Course Contents

  • Introduction: What is MI, and what it is not
  • Standards: Overview and HL7
  • Standards: DICOM
  • Making sense of standards
  • Computed tomography; Image enhancement
  • Computer-Aided Diagnostics (CAD)
  • Networking and teleradiology
  • Security
  • Scheduling and queuing
  • Simulation/Modeling in Medicine
  • Clinical software development; Medical startups
  • Medical startups
  • Unusual applications
Assessment Elements

Assessment Elements

  • non-blocking Class homework/projects, assigned after each lecture
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    0.8 * Class homework/projects, assigned after each lecture + 0.2 * Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Pianykh, O. S. Digital imaging and communications in medicine (DICOM): a practical introduction and survival guide. – Springer Science & Business Media, 2009. – 417 pp.

Recommended Additional Bibliography

  • Pianykh O. S. Digital Image Quality in Medicine. – Springer International Publishing, 2014. – 140 pp.

Authors

  • PYANYKH OLEG STANISLAVOVICH
  • Антропова Лариса Ивановна