SylabUZ
| Course name | Automated Medical Diagnosis System |
| Course ID | 06.9-WM-ER-IB-31_18 |
| Faculty | Faculty of Engineering and Technical Sciences |
| Field of study | WM - oferta ERASMUS |
| Education profile | - |
| Level of studies | Erasmus programme |
| Beginning semester | winter term 2018/2019 |
| Semester | 1 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
|
| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Credit with grade |
| Laboratory | 30 | 2 | - | - | Credit with grade |
- familiarize students with the methods of data collection used in medical diagnosis and the development of skills in the pre-processing of medical data
- familiarize students with the architecture of medical data warehouse and development of skills in the designing and application of analytical systems for medical data
- familiarize students with the methods used to build automated medical diagnosis systems and development of skills allowing the use of decision support and data mining algorithms
medical imaging techniques, digital signal processing, statistical methods of data analysis
Methods of data acquisition and processing for automated medical diagnosis.
Radiological imaging. Virtual microscopy. Application of image segmentation algorithms for the extraction of morphometric features. Feature selection methods. Discovering outliers. Completing the missing data.
Methods of storage and analysis of medical data. Medical data warehouse architecture. Analytical systems. Multidimensional data structures. Statistical analysis. Reporting methods and services. Analytical systems review. Overview of public repositories of medical data.
Medical decision support systems. Expert systems. Methods of knowledge representation. Methods of knowledge discovery. Classification algorithms. Artificial intelligence methods.
Medical decision support systems - case studies. Integration of decision support systems with picture archiving and communication systems.
Lectures - conventional lecture, discussion
Laboratory - laboratory exercises, case studies
| Outcome description | Outcome symbols | Methods of verification | The class form |
1. Piętka E.: Zintegrowany system informacyjny w pracy szpitala, PWN, 2004.
2. Rudowski R. (red.): Informatyka medyczna, PWN, 2003.
3. Cytowski J., Gielecki J., Gola A.,: Cyfrowe przetwarzanie obrazów medycznych. Algorytmy technologie zastosowania., AOW EXIT,2008
4. Nieniewski M.: Segmentacja obrazów cyfrowych. Metody segmentacji wododziałowej. AOW EXIT, 2005.
5. Hand D., Mannila H., Smyth P.: Eksploracja danych. WNT, 2005.
6. Kisielnicki J., Pańkowska M., Sroka H.: Zintegrowane systemy informatyczne. Dobre praktyki wdrożeń, Warszawa, PWN, 2011.
7. Larose T. D.: Odkrywanie wiedzy z danych, Warszawa, PWN, 2006.
8. Larose D. T.: Metody i modele eksploracji danych, Warszawa, PWN, 2008.
9. Pelikant A.: Hurtownie danych. Od przetwarzania analitycznego do raportowania, Helion, 2011.
10. Kącki E., Kulikowski J.L., Nowakowski A., Waniewski E. (red.): Systemy komputerowe i teleinformatyczne w służbie zdrowia. AOW EXIT, 2000.
11. Zajdel R., Kęcki E., Szczepaniak P., Kurzyński M.: Kompendium informatyki medycznej, Alfa-Medica Press, 2003.
12. Cierniak J.: Tomografia komputerowa. Budowa urządzeń CT. Algorytmy rekonstrukcyjne.
13. Klonecki W.: Statystyka dla inżynierów. PWN. 1999.
14. Cantor A.B.: Survival Analysis Techniques for Medical Research. SAS, 2007.
15. Suri J. S., Setarehdan K, Singh S. (red.): Advanced Algorithmic Approaches to Medical Image Segmentation. Springer, 2002.
Modified by dr hab. inż. Katarzyna Arkusz, prof. UZ (last modification: 19-04-2018 11:47)
