Technologies and methods for the analysis of biomedical data

Description: he discipline is devoted to the study of basic The discipline is devoted to the study of basic machine learning algorithms with an emphasis on their mathematical description and features of application to biomedical data. The issues of data preparation, selection of significant features, analysis of variance, decision trees and forests, neural networks and BigData technologies are considered.

Amount of credits: 6

Пререквизиты:

• Fundamentals of Informational-Instrumentation Technology

Course Workload:

Component: University component

Cycle: Profiling disciplines

Goal

• To develop knowledge and skills in analyzing medical and biological data in master's students, to apply modern methods of information processing in medicine and biology, and to interpret results using specialized software.

Objective

• Mastering the basics of biomedical data analysis
• Mastering statistical methods in medical data analysis
• Study of machine learning and predictive modeling methods
• Mastering tools for processing and visualizing medical data

Learning outcome: knowledge and understanding

• To define scientific methods of systems theory and systems analysis in research and development of modern complex systems.
• Describe the specifics of the theory of presentation of biomedical information.

Learning outcome: applying knowledge and understanding

• Classify methods of processing and analyzing medical information, and use them correctly when analyzing medical and biological data.

Learning outcome: formation of judgments

• Select the necessary mathematical models that underlie various approaches to solving biomedical problems associated with processing large volumes of data.

Learning outcome: communicative abilities

• Present data analysis results in graphs and reports, and explain findings and decisions to clinicians and researchers.

Learning outcome: learning skills or learning abilities

• Work with modern analytical tools.
• Master new methods of medical data analysis.

Teaching methods

- interactive lecture (use of the following active forms of learning: guided (controlled) discussion or conversation; moderation; demonstration of slides or educational films; brainstorming; motivational speech)

- construction of scenarios for the development of various situations based on given conditions

- information and communication (for example, classes in a computer lab using professional software packages)

- search and research (independent research activities of students during the learning process)

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Introduction to the analysis of medical and biological data
• Methods of data preprocessing and filtering
• Fundamentals of machine learning and statistics for medical data analysis
• Processing and analysis of biomedical signals
• Methods of pathology recognition
• Methods for clustering and classification of medical data
• Methods for visualizing medical data
• Fundamentals of predictive modeling in medicine
• Automated systems for analyzing medical data

Key reading

• Frolov A.V.Cifrovaya obrabotka biomedicinskih signalov i izobrazhenij. – Moskva: MGTU im. N.E. Baumana, 2020. – 400 s.
• Kublanov V.S., Dolganov A.Yu., Kostousov V.B.Biomedicinskie signaly i izobrazheniya v cifrovom zdravoohranenii: hranenie, obrabotka i analiz. – Ekaterinburg: UrFU, 2021. – 350 s.
• Kras'ko O.V. Statisticheskij analiz dannyh v medicinskih issledovaniyah / O. V. Kras'ko. – Minsk: MGEU im. A.D. Saharova, 2024. – Ch. I. – 127 s.
• V.N.Kanyukov, A.A. Stadnikov, O.M. Trubina, A.D. Strekalovskaya Metody issledovaniya v biologii i medicine: uchebnik / V.N.Kanyukov, A.A. Stadnikov, O.M. Trubina, A.D. Strekalovskaya; Orenburgskij gos. un-t. – Orenburg: OGU, 2013. – 192s.

Further reading

• Vasil'ev V.G., Kuzhen'kin S.N.Prikladnaya obrabotka biomedicinskih izobrazhenij v srede MATLAB. – Sankt-Peterburg: Lan', 2019. – 280 s.
• Makarova N.V. Statisticheskij analiz mediko-biologicheskih dannyh s ispol'zovaniem paketov statisticheskih programm Statistica, SPSS, NCSS, SYSTAT: metodicheskoe posobie / N.V. Makarova; Vseros. centr ekstren. i radiac. mediciny im. A.M. Nikiforova MChS Rossii – SPb.: Politekhnika-servis, 2012. – 178 s.
• Men'shikov V.V., Kliniko-laboratornye analiticheskie tekhnologii i oborudovanie./ Men'shikov V.V. - M. : Akademiya, 2007 – 240 s.
• Otkrytye kursy (MOOK) po mashinnomu obucheniyu na Coursera – https://www.coursera.org/courses?query=machine%20learning