PhD courses
Speciality Name:
Аrtificial Intelligence in Medicine
Faculty:
Information technology
Qualification:
- Scientific and pedagogical direction - Doctor of Philosophy (PhD)
- Model of graduating student
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Mandatory
disciplines
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Elective
disciplines
- Professional
Build mathematical models of various tasks of creating a public good, determine a methodology for applying artificial intelligence methods to them, set quality assessment criteria, develop general data models and organize data exchange based on cloud computing in order to increase the likelihood of socially beneficial results.
2. Compare and select digital signal processing algorithms for various medical applications, evaluate experimental results and correlate them with appropriate design and programming methods, implement digital signal processing algorithms and design methods on embedded devices.
3. Perform the main stages of preparing medical imaging data when developing artificial intelligence algorithms, explain the current limitations for data processing, and explore new approaches to solving data accessibility problems.
4. Apply machine learning methods for medical diagnostics and analytics based on medical data, create tools for data mining.
5. Evaluate how embedded systems, artificial intelligence tools for medical care can be used to identify and assess the health effects of behavioral and environmental factors.
6. Draw up research programs, apply research methods, carry out scientific management of research on the most important scientific problems of a fundamental and applied nature, obtain the necessary data from scientific and technical documents, reports and other reference materials.
7. Conduct teaching activities in higher education institutions, introduce advanced and innovative teaching technologies, develop educational and methodological support for new courses, taking into account the social modernization of Kazakhstan and the development of the national economy.
8. To contribute in the framework of original studies that expand the boundaries of knowledge through the use of artificial intelligence in medicine, use the academic style of writing, publish research results in the form of scientific articles in Kazakh and foreign publications, be prepared for correct and tolerant interaction in society, for social interaction and cooperation to solve scientific and technical problems.
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Academic writing
- Number of credits: 2
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to write scientific texts, mainly research articles, apply strategies and skills of text analysis.
As a result of studying the discipline, students have the ability to form:
- structure your ideas in order to write clearly formulated sentences and coherent paragraphs;
- use the academic style of writing, characterized by an accurate, concise and formal language;
- report on previous studies and evaluate their importance;
- recognize and use various methods in academic texts;
- structurally present the results of scientific research in terms of the choice of journal, the type of publication or the relative value of the news of its findings.
The following aspects are considered within the discipline: The main aspects of academic writing for doctoral students. The structure of the scientific article of the original research type “Introduction, methods, results, analysis and discussion” (IMRAD). Differences in the structure and organization of scientific work. Design sections of a scientific article: conducting, literature review, goals, progress report, plans for the future, links. Ways to link ideas and arguments. Critical assessment. Methods of strict concise writing. Analysis and synthesis in academic writing. Reviewing scientific papers.
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Artificial Intelligence for Social Good
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to use artificial intelligence and machine learning for the public good, in particular, to solve environmental, health and social issues. The following aspects are considered within the discipline: Introduction to the public good. Mathematical foundations and technologies for solving problems of the public good: optimization problems, regressions, convolutional neural networks, recurrent neural networks. Transforming the ideas of artificial intelligence into a real social impact. Machine learning methods in social issues. Computer vision in social problems. Natural language processing in social issues. The use of artificial intelligence in green energy, ecology, inclusion, medicine.
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Scientific Research methods
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline: consists in the development of scientific thinking and research skills of doctoral students, the use of scientific research methods in the field of scientific interests.
As a result of studying the discipline, students have the ability to form:
- describe the basic concepts of scientific research and its methodology;
- determine the relevant research topics, select and determine the relevant research tasks and their parameters;
- to carry out the development and research of theoretical and experimental models of information resources;
- analyze the results of experiments, make the choice of optimal solutions, compile reviews, reports and prepare scientific publications.
The following aspects are considered within the discipline: an introduction to the research methodology: an overview of the fundamental foundations. Research Problem: Scientific Thinking. Literature review: the importance of literature review, needs, goals, sources, functions of literature. Research hypotheses: meaning, definitions, nature, functions, significance, types of hypotheses, variables in a hypothesis, formulation of a hypothesis, testing of a hypothesis. Research approach: philosophical background, qualitative and quantitative approach, a mixed methodological approach. Research strategies: experiments, ethnography, phenomenology, grounded theory, practical research.
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Advanced Digital Signal Processing
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to apply technologies for processing medical signals using Fourier transform, spectral analysis and signal filtering. Within the discipline the following aspects will be considered: Brief information about the development of methods for processing biomedical signals and data. The role of automation of processing and analysis of biomedical signals in improving medical diagnostics. Presentation of data: production and presentation of biomedical data. Signal splines. Preprocessing methods for encoding medical data. Errors in digital signal processing (DSP) methods. Digital filtering. Digital filter. Smoothing and designing filters. Adaptive filtering. Data compression. Creating virtual signal compression devices. Biomedical signals. Syntactic recognition of signals. Digital technologies in medicine. Modern DSP and the Internet of things
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PhD thesis writing and defence
- Number of credits: 12
- Type of control: Докторская диссертация
- Description: The main purpose of "PhD thesis writing and defence": of a doctoral dissertation is the formation of the doctoral students' ability to disclose the content of research work for the defense of the thesis.
During the study of course, doctoral student's should be competent in:
1. to substantiate the content of new scientifically grounded theoretical and experimental results that allow to solve a theoretical or applied problem or are a major achievement in the development of specific scientific directions;
2. explain the assessment of the completeness of the solutions to the tasks assigned, according to the specifics of the professional sphere of activity;
3. they can analyze alternative solutions for solving research and practical problems and assess the prospects for implementing these options;
4. apply the skills of writing scientific texts and presenting them in the form of scientific publications and presentations.
5. to plan and structure the scientific search, clearly highlight the research problem, develop a plan / program and methods for its study, formalize, in accordance with the requirements of the State Educational Establishment, the scientific and qualification work in the form of a thesis for a scientific degree Doctor of Doctor of Philosophy (PhD) on specialty «8D07502 – Standardization and certification (by industry)».
During the study of the discipline doctoral student will learn following aspects: Registration of documents for presentation of the thesis for defense. Information card of the dissertation and registration-registration card (in the format Visio 2003). Extract from the minutes of the meeting of the institution, in which the preliminary defense of the thesis took place. Cover letter to the Higher Attestation Commission. Expert conclusion on the possibility of publishing the author's abstract. Expert opinion on the possibility of publishing a dissertation. Minutes of the meeting of the counting commission. Bulletin for voting. A shorthand record of the meeting of the dissertational council. List of scientific papers. Response of the official opponent. A review of the leading organization. The recall of the scientific adviser.
Data for 2021-2024 years
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Embedded systems and their applications in healthcare
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to use embedded systems to solve practical problems in healthcare with modern tools. Within the discipline the following aspects will be considered: Embedded system. Real-time mechanisms. Embedded computing systems. Features of high-level VSS construction. 5 VSS Architectural design. In a faceted model of the process of creating VSS. Technical means of the FSS. The modular principle of organization of the WSS. VSS network interfaces. FSS software tools. Programming languages: language Requirements for control systems. VSS debugging and testing tools. Software development: Features of embedded systems design. The device of a modern controller on the example of SDK-1.1. liquid crystal indicator. External memory. Tools for the SDK-1.1. Examples of programming the SDK-1.1 stand.
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Deep Learning for Medical Imaging
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of discipline: consists in forming the ability to build machine learning models for visualization and diagnosis based on medical images. The following aspects are considered within the discipline: Introduction to medical imaging. The basics of medical imaging. Visualization of the results of projection radiography: radiation, electrons, ionization, equipment, electrons, side effects, detection and diagnosis of bone fractures. Visualization of computed tomography (CT) results: terminology and equipment, sonograms, presentation of CT data, image reconstruction. Ultrasound imaging: system architecture, components, terminology, note on refraction and speed of sound, imaging and typical application, artifacts, advanced techniques. Visualization of the results of magnetic resonance imaging: coils, inverted protons, Faraday induction, visualization of neurological diseases.
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Machine Learning for Medical Diagnosis
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to apply machine learning methods in the diagnosis of medical diseases. The following aspects are considered within the discipline: Introduction to medical diagnostics. Detection of diseases using computer vision. Building and training a model of medical diagnostics. Image classification and class imbalance. CNN architecture. Work with a small training set. Model testing. Indicators of sensitivity, specificity and evaluation. ROC curve and threshold value. Segmentation of medical images. 2D U-Net and 3D U-Net image segmentation. Increased data volume for segmentation. Loss function for image segmentation. Various populations and diagnostic technologies. Measurement of patient treatment results.
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Applied Electrical and Electronic Engineering for Medicine
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to solve physical and technical problems in the field of medical electronics and biomedical diagnostics. Within the discipline the following aspects will be considered: Electrical measurements in medicine. Medical measuring instruments. Electrodes and microelectrodes: Electrodes of electrocardiographs and electroencephalographs. Electro-organism system: Equivalent substitution schemes of the electrode-organism system. Resistive sensor. The photovoltaic devices. Semiconductor photo converters. Thermoelectric converter. Medical device: The use of photodetectors sensitive to infrared radiation to measure the temperature of the skin. Piezoelectric transducer. Measuring amplifiers and filters. Functional units of electronic devices for medical purposes. Structure and circuitry of diagnostic and therapeutic devices. Measuring and recording channels. Electronic electrical stimulators.
Data for 2021-2024 years
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Pedagogical
- Type of control: Защита практики
- Description: Aim оf discipline: formation of the ability to carry out educational activities in universities, to design the educational process and conduct certain types of training sessions using innovative educational technologies.
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Research
- Type of control: Защита практики
- Description: The purpose of the practice: gaining experience in the study of an actual scientific problem, expand the professional knowledge gained in the learning process, and developing practical skills for conducting independent scientific work. The practice is aimed at developing the skills of research, analysis and application of economic knowledge.
Data for 2021-2024 years
