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Regular version of the site
Master 2021/2022

Medical Informatics

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Elective course (Data Science)
Area of studies: Applied Mathematics and Informatics
When: 1 year, 1, 2 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Oleg Pianykh
Master’s programme: Data Science
Language: English
ECTS credits: 4
Contact hours: 50

Course Syllabus

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

  • 2021/2022 2nd module
    The class grade is computed as 80% of homeworks/projects + 20% of the final exam. In addition to this, student attendance, originality of work and contributions to the class will be taken into account, especially for those balancing between in between two different grades.
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.