Faculty of Health Sciences · Nursing · Undergraduate
ECTS: 3 T+P+L: 2+0+0 Departmental Elective
Coordinator: Dr. Öğr. Üyesi Sahra TİLKİ
Instructors: Dr. Öğr. Üyesi Sahra TİLKİ
Course Objective
The objective of this course is to introduce students to the applications of artificial intelligence (AI) and machine learning (ML) technologies in the healthcare field; to teach the fundamental methods used in the processing, analysis, and interpretation of health data; and to provide knowledge and skills regarding clinical decision support systems, patient monitoring, medical imaging, natural language processing, and ethical/legal dimensions.
Course Content
This course focuses on the fundamental concepts, methods, and application areas related to the use of artificial intelligence and machine learning techniques in the healthcare field.
Course Learning Outcomes
- Explain the fundamental concepts of artificial intelligence and machine learning (ML) at both theoretical and practical levels.
- Defines AI application areas in nursing care processes (triage, early warning, patient monitoring, care planning).
- Defines basic data preprocessing, classification, and regression models.
- Identify ethical, privacy (KVKK/GDPR), data quality, and security issues when working with clinical data.
Core Area Distribution
(52) Engineering and Engineering Trades%50 (72) Health%50
Teaching Methods
ExpressionQuestion-AnswerDiscussionExercise and PracticeGroup StudyBrain StormingCase StudySelf studyProblem Solving
Assessment & Evaluation
Project / DesignTesting (Essay / Tests: True-Falls, multiple-choice, short answer, matching)
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 2 | 32 |
| Out of Class Study Period | 0 | 0 | 0 |
| Midterm | 1 | 2 | 2 |
| Quiz | 0 | 0 | 0 |
| Assignment | 0 | 0 | 0 |
| Practice | 1 | 5 | 5 |
| Final | 1 | 2 | 2 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Artificial Intelligence and Nursing | - |
| 2 | Data and Health Data Characteristics | Week 1 |
| 3 | Fundamental Statistics and Data Preprocessing | Week 2 |
| 4 | Introduction to Machine Learning: Supervised Learning | Week 3 |
| 5 | Introduction to Machine Learning: Supervised Learning (continuous) | Week 4 |
| 6 | Model Evaluation and Performance Metrics | Week 5 |
| 7 | Natural Language Processing (NLP) and Health Texts | Week 6 |
| 8 | Midterm | Repeat 1.-7. Weeks |
| 9 | Time Series and Patient Monitoring (Wearables, Monitor Data) | Week 7 |
| 10 | Fundamentals of Image Processing (Basic Level) | Week 9 |
| 11 | Fundamentals of Image Processing (Basic Level) | Week 10 |
| 12 | Explainability (XAI) and Clinical Reliability | Week 11 |
| 13 | Ethics, Law, and Human Factors | Week 12 |
| 14 | Project presentation | - |
| 15 | Project presentation | i- |
| 16 | Final | Repeat 1. - 15. Weeks |