TÜRKÇE
ENGLISH
Information
| Code | Trans621 |
| Name | The use of Artificial Intelligence in Translational Medicine |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
The aim of this course to provide an understanding of the working principles of artificial intelligence and its use in translational medicine, based on the algorithms of computer programming.
Course Content
Artificial intelligence working principles and the use of artificial Intelligence in translational medicine: Computer Programming and Algoritm, Today's Technology, Automation Levels and Principles, Automation Mechanisms, Basic Principle of Digital Signal Processing, Adaptive Automation, Human-Machine Task Sharing in Automation Systems (Function Allocation), Artificial Intelligence, Artificial Intelligence-Human Nervous System Relationship, Machine Learning, Deep Learning, The use of Artificial Intelligence in Various Areas, Use of Artificial Intelligence in Translational Medicine, Problem Solving in Translational Medicine using Artificial Intelligence
Course Precondition
None
Resources
Lecture Notes and Power Point Slides
Notes
Internet, Library Database, and Reccomended Sources
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Conceives algorithm for computer programming. |
| LO02 | Compares the innovations brought by Today's technology. |
| LO03 | Defines the automation levels and principles. |
| LO04 | Explains the mechanisms of automation. |
| LO05 | Explains and applies the basic principles of Digital Signal Processing. |
| LO06 | Differentiates the adaptive automation systems. |
| LO07 | Understands how man-machine task sharing should be in the automation system, and gives an example of task sharing. |
| LO08 | Knows how the artificial intelligence works, and understands machine laerning and deep learning. |
| LO09 | Compares artificial intelligence and human nervous system. |
| LO10 | Explains the areas where artificial intelligence is used, and evaluates how to use it in translational medicine. |
| LO11 | Tries to solve a problem in translational medicine using artificial intelligence by integrating the knowledge of artificial intelligence. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Develops and deepens the current and advanced level knowledge of translational medicine via unique thoughts or researches at a level of proficiency, find out original definitions that will bring innovation in the field of translational medicine. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Conceives the interdisciplinary interactions related to translational medicine; analyzes synthesizes and evaluates original and new thoughts. | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explains the usage of tool, devices and instruments requiered for knowledge and technologies about translational medicine and its related disciplines. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Defines frequently used statistical methods in translational medicine and related disciplines, uses statistical softwares effectively. | 3 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Uses both theoretical and practical knowledge at an advanced level in the studies related to translational medicine. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops a new thoughts, method and designment/application which brings innovation in translational medicine or implements a known thoughts, method and designment/application in different fields, investigates, comprehends, designs, adapts, implements an original topic. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Writes the report of his/her research which he/she participated, | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Makes necessary investigations by using tool, devices and instrument required for knowledge and technologies about translational medicine and related disciplines at an advanced level, develops a new and creative solution (device, method, treatment, drug) for the problems. | 4 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Uses statistical software in the field of Translational Medicine effectively, chooses statistical methods correctly, calculates and interprets correctly. | 3 |
| PLO10 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Maintains the organization of Translational Medicine laboratories and develops solutions in case of encountering unforeseen complex situations during laboratory working hours. | 3 |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Conducts scientific studies in translational medicine and related fields independently or as a team member. | 4 |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Writes the report of his/her research in which he/she participated, publishes in a national or international reputed journal (indexing in SCI, SCI-Expanded, SSCI, or AHCI), and presents it at scientific meetings. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Follows evidence-based practices and conducts research on professional practices that will create evidence in their field. | 4 |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Applies the principles of advanced professional development and lifelong learning in the field of Translational Medicine. | 5 |
| PLO15 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Communicate current developments and studies within the field to both professional and non-professional groups systematically using written, oral and visual techniques by supporting with quantitative and qualitative data. | 5 |
| PLO16 | Bilgi - Kuramsal, Olgusal | Learns how to teach. | 5 |
| PLO17 | Belirsiz | Communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level. | |
| PLO18 | Yetkinlikler - Alana Özgü Yetkinlik | Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and uses these issues for social strategy, implementation plans and frame of quality processes. | 5 |
| PLO19 | Yetkinlikler - Alana Özgü Yetkinlik | Knows the importance of ethical principles and ethical committees for the individual and society, acts ethically. | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Computer Programming and Algorithm | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 2 | Today's Technology | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 3 | Automation Levels and Principles | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 4 | Automation Mechanisms | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 5 | Digital Signaling Process | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 6 | Adaptive Automation Systems | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 7 | Brain-Computer Interaction | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 8 | Mid-Term Exam | Integration of knowledge from different units | Ölçme Yöntemleri: Ödev |
| 9 | Function Allocation | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 10 | Artificial Intelligence | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 11 | Machine Learning | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 12 | Deep Learning | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 13 | The Use of Artificial Intelligence in Different Areas and in Translational Medicine | Reading recommended sources | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 14 | Solving a problem in Translational Medicine Using Artificial Intelligence. | Integration of knowledge from different units | Öğretim Yöntemleri: Soru-Cevap, Problem Çözme, Örnek Olay |
| 15 | Solving a problem in Translational Medicine Using Artificial Intelligence (Presentation) | Integration of knowledge from different units | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Problem Çözme, Örnek Olay |
| 16 | Term Exams | Integration of knowledge from different units | Ölçme Yöntemleri: Ödev, Performans Değerlendirmesi |
| 17 | Term Exams |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 10 | 10 |
| Final Exam | 1 | 16 | 16 |
| Total Workload (Hour) | 125 | ||
| Total Workload / 25 (h) | 5,00 | ||
| ECTS | 5 ECTS | ||