Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN413Machine Learning for HealthCare3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The objective of this course is to provide students with the general concepts and problems in the field of medicine, together with the intelligent systems that are developed and applied to solve these problems, at an introductory level. In this sense, artificial learning approaches used in the analysis of biomedical data will be explained and discussed, accompanied with current applications from the field.
Course Content Types, structures and properties of data produced in the biomedical field, knowledge representation, clinical risk stratification, biomarker discovery, time series analysis of physiological data, patient outcome prediction, disease progression modelling, cancer detection analysis, big data approaches in health. Also, artificial learning based application examples for all of the topics listed above.
Course Methods and Techniques Lecture, discussion, question and answer, preparing and/or presenting reports, problem solving
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Instructor Bölüm Sorumluları
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Cleophas, T. J. & Zwinderman, A. H. (2015). Machine Learning in Medicine - a Complete Overview. Springer. Natarajan, P., Frenzel, J. C., & Smaltz, D. H. (2017). Demystifying big data and machine learning for healthcare. CRC Press.
Course Notes Cleophas, T. J. & Zwinderman, A. H. (2015). Machine Learning in Medicine - a Complete Overview. Springer.

Natarajan, P., Frenzel, J. C., & Smaltz, D. H. (2017). Demystifying big data and machine learning for healthcare. CRC Press.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 25
Assignment 3 % 30
Attendance 1 % 10
Final examination 1 % 35
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 3 8 24
Preparation for Midterm Exam 1 30 30
General Exam Preparation 1 40 40
Total Work Load   Number of ECTS Credits 5,93333333333333 178

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 In completion of this course the students: 1- will have an understanding about the terminology and basic concepts used in the field of biomedicine
2 2- will have the ability of developing and applying computational models to solve problems in the domains of healthcare and biomedicine in general
3 3- can qualitatively and quantitatively evaluate developed models using performance analysis methods and find the most efficient modelling approach specific to the problem at hand via comparative assessments.
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Basic and informatics concepts in healthcare and health technologies, terminology, data types and structure
2 Introduction and examination of the foundational concepts in mathematics and statistics that will be used throughout the course
3 Review of foundational artificial learning concepts that are frequently utilised in health related applications
4 The concept of clinical risk, risk stratification and disease modelling
5 Clinical decision process and decision support systems, evidence-based medicine and its applications
6 Phenotype studies, biomarker discovery, personalized medicine approaches
7 Midterm
8 Processing medical images and the other type of biomedical signals, tissue analysis and the artificial learning applications
9 Ontological knowledge systems and annotation in healthcare, their usage as source data in artificial learning models and the design of automated annotation (prediction) models
10 Acquisition and analysis of personalized molecular data in medicine and the use of artificial learning models, which utilise this data, in diagnosis and therapy
11 Data mining and artificial learning applications in cancer studies
12 Evaluation of artificial learning models: performance metrics, validation, model interpretability
13 Current Technologies and computational tools for artificial learning applications in healthcare
14 Discussion of data safety, security and the ethics data usage in healthcare, in the axis of artificial learning applications
15 Review and discussion of topics and subjects from previous weeks
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 2 4 5 5 4 4 3 4 2 5
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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