TÜRKÇE
ENGLISH
Information
Course Goal / Objective
This course introduces the applications of artificial intelligence (AI) and machine learning (ML) in healthcare. Students will learn about the fundamental principles of AI, types of algorithms, healthcare data analysis, the use of AI in diagnostic and therapeutic support systems, ethical issues, and current best practices.
Course Content
This course introduces the applications of artificial intelligence (AI) and machine learning (ML) in healthcare. Students will learn about the fundamental principles of AI, types of algorithms, healthcare data analysis, the use of AI in diagnostic and therapeutic support systems, ethical issues, and current best practices.
Course Precondition
None
Resources
Lecture notes to be given by the instructor
Notes
Lecture notes to be given by the instructor
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Describes artificial intelligence applications used in health informatics. |
| LO02 | Analyzes artificial intelligence algorithms at a basic level. |
| LO03 | Evaluates the ethical aspects of artificial intelligence systems working with health data. |
| LO04 | Develops a simple AI solution for a health problem. |
| LO05 | Explains the basic concepts and techniques of artificial intelligence. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Defines the concept of health informatics. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains the types and sources of health data. | |
| PLO03 | Belirsiz | Analyzes the processing, storage and sharing of health data | |
| PLO04 | Belirsiz | Summarizes the structure and function of health information systems. | 4 |
| PLO05 | Belirsiz | Evaluates the effects of digitalization in healthcare. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to artificial intelligence and basic concepts | Lucture Notes | Öğretim Yöntemleri: Soru-Cevap |
| 2 | Introduction to machine learning and deep learning | Lucture Notes | Öğretim Yöntemleri: Soru-Cevap |
| 3 | Overview of artificial intelligence in health informatics | Lucture Notes | Öğretim Yöntemleri: Beyin Fırtınası |
| 4 | Collection and preparation of health data | Lucture Notes | Öğretim Yöntemleri: Tartışma |
| 5 | Classification algorithms and their usage areas | Lucture Notes | Öğretim Yöntemleri: Soru-Cevap |
| 6 | Health data analysis with regression algorithms | Lucture Notes | Öğretim Yöntemleri: Beyin Fırtınası |
| 7 | Image recognition systems (Radiology examples) | Lucture Notes | Öğretim Yöntemleri: Soru-Cevap |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Natural Language Processing (NLP) and patient records | Lucture Notes | Öğretim Yöntemleri: Anlatım |
| 10 | Clinical decision support systems | Lucture Notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Ethical and legal aspects of artificial intelligence applications | Lucture Notes | Öğretim Yöntemleri: Örnek Olay, Soru-Cevap |
| 12 | Simple AI project planning | Lucture Notes | Öğretim Yöntemleri: Tartışma, Anlatım |
| 13 | Application example: Patient risk classification model | Lucture Notes | Öğretim Yöntemleri: Soru-Cevap |
| 14 | End-of-term general evaluation and project presentations | Lucture Notes | Öğretim Yöntemleri: Soru-Cevap |
| 15 | Project presentation | Lucture Notes | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 1 | 14 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 1 | 1 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 1 | 1 |
| Final Exam | 1 | 1 | 1 |
| Total Workload (Hour) | 73 | ||
| Total Workload / 25 (h) | 2,92 | ||
| ECTS | 3 ECTS | ||