Automation of biomedical information processing

Description: The discipline is devoted to the study of ways of presenting experimental information of various physical nature in biomedical practice; mathematical models underlying the methods of processing and analyzing information; mathematical methods and algorithms for assessing the information content of parameters; innovative technologies used in the automation of biomedical research.

Amount of credits: 5

Пререквизиты:

• Integral and Microprocessor-based Circuit Engineering

Course Workload:

Component: Component by selection

Cycle: Base disciplines

Goal

• developing in master's students the competencies necessary for the development, implementation and application of automation methods and technologies in the processing of biomedical data.

Objective

• Study of the principles of automation of the processes of collecting, processing and analyzing data obtained from biomedical research and diagnostics.
• Study of algorithms for automated analysis of biomedical data, which increases the accuracy and efficiency of diagnostics, as well as support for clinical decision-making.
• Mastering modern technologies and software for automating the analysis of biomedical information, including artificial intelligence systems, machine learning and big data analysis.
• Mastering approaches to integrating biomedical data from various sources, ensuring compatibility and standardization of data for their more effective use in digital medicine.

Learning outcome: knowledge and understanding

• Describe the principles and methods of automating biomedical data processing, including the use of artificial intelligence, machine learning, and big data analytics technologies to solve medical problems.
• Discuss the system and architectural features of information systems designed to process and integrate biomedical data, and demonstrate their compatibility with medical informatics standards.

Learning outcome: applying knowledge and understanding

• Develop algorithms and software modules for automated analysis and processing of biomedical data, for example, for diagnostic, monitoring, and prognostic purposes.
• Apply big data analytics methods to identify hidden patterns in biomedical data and build models that can support clinical decisions.
• Integrate biomedical data from various sources, including health monitoring devices, laboratory data, and electronic medical record data, while maintaining interoperability and security standards.

Learning outcome: formation of judgments

• Assess the accuracy and reliability of data, identify and correct processing errors to ensure the quality of data that supports diagnosis and treatment.

Learning outcome: communicative abilities

• Present the results of analysis and interpretation of biomedical data, including explaining complex technical aspects to non-technical people.

Learning outcome: learning skills or learning abilities

• Apply tools and technologies to automate biomedical data processing, including software and platforms for data analysis, visualization, predictive analytics, and medical decision support systems.

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 Automation of Biomedical Information Processing
• Fundamentals of Medical Information Systems
• Digital Processing of Biomedical Signals and Images
• Algorithms for Preprocessing Medical Data
• Methods of Intelligent Data Analysis in Medicine
• Automated Decision Support Systems
• Integration of Data from Medical Sensors and Wearable Devices
• Software Tools for Processing Biomedical Information
• Machine Learning Technologies in Medical Data Analysis

Key reading

• 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.
• Omel'chenko V.P., Demidova A.A. Medicinskaya informatika: uchebnik. - GEOTAR-Media, 2023. 528s.

Further reading

• Frolov A.V.Cifrovaya obrabotka biomedicinskih signalov i izobrazhenij. – Moskva: MGTU im. N.E. Baumana, 2020. – 400 s.
• Vasil'ev V.G., Kuzhen'kin S.N.Prikladnaya obrabotka biomedicinskih izobrazhenij v srede MATLAB. – Sankt-Peterburg: Lan', 2019. – 280 s.
• Sergienko, A. B. Cifrovaya obrabotka signalov: uchebnoe posobie / A. B. Sergienko. - 2-e izd. - SPb. : Piter, 2006. - 751 s.
• Dianov S. V. Avtomatizaciya obrabotki eksperimental'nyh dannyh: konspekt lekcij. - Vologda: VoGTU, 2010. - 82 s.