Module name:
Modeling biomedical systems
Academic year:
Faculty of:
2032/2033
MIS-2-308-IH-n
ECTS credits:
5
Metals Engineering and Industrial Computer Science
Field of study:
Study level:
Code:
Applied Computer
Science
Specialty: Computer Science in Metallurgical
Engineering
Second-cycle studies
Lecture language:
English
Form and type of study:
Profile of education:
Part-time studies
Academic (A)
Semester:
3
Course homepage:
Responsible teacher:
prof. dr hab. inż. Rońda Jacek ([email protected])
Academic teachers: prof. dr hab. inż. Rońda Jacek ([email protected])
Description of learning outcomes for module
MLO code
Student after module completion has the
knowledge/ knows how to/is able to
Connections with FLO
Method of learning
outcomes verification
(form of completion)
Student knows how to prepare
computational model for bioengineering
and presentations about modeling in
bioengineering
IS2A_U01, IS2A_U03, IS2A_U11,
IS2A_U20
Activity during classes
M_W001
Student has knowledge about
bioengineering
IS2A_U01, IS2A_U03, IS2A_U07,
IS2A_U08, IS2A_U09, IS2A_U16
Examination
M_W002
Student has knowledge about
ontologies, reasoning and logic
IS2A_U02
Scientific paper
M_W003
Student has knowledge about cognitive
biases
IS2A_W01, IS2A_W02,
IS2A_W03, IS2A_W08,
IS2A_W17, IS2A_W23
Examination
Skills
M_U001
Knowledge
FLO matrix in relation to forms of classes
1/5
Module card - Modeling biomedical systems
Conversation
seminar
Seminar
classes
Practical
classes
Fieldwork
classes
-
-
-
-
-
+
-
-
-
-
-
M_W001
Student has knowledge about
bioengineering
+
-
-
-
-
-
-
-
-
-
-
M_W002
Student has knowledge about
ontologies, reasoning and
logic
+
-
-
-
-
-
-
-
-
-
-
M_W003
Student has knowledge about
cognitive biases
+
-
-
-
-
-
-
-
-
-
-
Others
E-learning
Project
classes
Student knows how to
prepare computational model
for bioengineering and
presentations about modeling
in bioengineering
Workshops
Laboratory
classes
Form of classes
Auditorium
classes
Student after module
completion has the
knowledge/ knows how to/is
able to
Lectures
MLO code
Skills
M_U001
Knowledge
Module content
Lectures
Introduction
Berners-Lee, T., Hendler, J., and Lassila, O. The Semantic,Web. Scientific American,
May 2001: http://www.scientificamerican.com/article.cfm?id=the-semantic-web.
Clinical Systems
-Ash, J.S., Berg, M., and Coiera. Some unintended consequences of information
technology in health care: The nature of patient care information system-related
errors. J Am Med Inform Assoc. 2004;11:104-112:
http://jamia.bmj.com/content/11/2/104.full.pdf+html,
-President’s Council of Advisors on Science and Technology. Realizing the Full Potential
of Health Information Technology to Improve Healthcare for Americans: The Path
Forward. December 2010:
http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-health-it-report.pdf
Classification Systems
-Vanopstal, K., Vander Stichele, R., Laureys, G., and Buysschaert. Vocabularies and
retrieval tools in biomedicine: Disentangling the terminological knot. Journal of Medical
Systems 35(4):527–543, 2009.
http://www.springerlink.com/content/72760286411×43kr/
-Mathews, A.W. Walked Into a lamppost? Hurt while crocheting? Help Is on the way.
Wall Street Journal, Sept. 13 2011. http://online.wsj.com/article/
SB10001424053111904103404576560742746021106.htmlNational Library of
Medicine. Unified Medical Language System Basics.
2/5
Module card - Modeling biomedical systems
http://www.nlm.nih.gov/research/umls/new_users/online_learning/OVR_001.htm
Conceptual Modeling
- Holland, I.M. and Lieberherr, K.J.Object-oriented design. ACM Computing Surveys
28(1), 1996. http://dl.acm.org/citation.cfm?id=234421
- Ahir, S.S. Understanding the Unified Modeling Language (UML).
—http://www.methodsandtools.com/archive/archive.php?id=76
Biomedical Modeling
-Smith, B., and Ceusters, W. HL7 RIM: An incoherent standard. Studies in Health
Technology and Informatics 124:133–138, 2006.
http://ontology.buffalo.edu/HL7/doublestandards.pdf
-Schadow, G., Mead, C.N., and Walker, M. The HL7 Reference Information Model under
Scrutiny. Studies in Health Technology and Informatics, 2006.
http://amisha.pragmaticdata.com/~schadow/Schadow-MIE06-r3.pdf
-Dolin, R.H., Alschuler, L., Boyer, S., et al. HL7 Clinical Document Architecture, Release
2. Journal of the American Medical Informatics Association 13:30–39, 2006.
http://jamia.bmjjournals.com/content/13/1/30.abstract
-Beale, T. Archetypes: constraint-based domain models for future-proof information
systems. OOPSLA Workshop on Behavioral Semantics, 2002.
http://www.openehr.org/publications/archetypes/archetypes_beale_oopsla_2002.pdf,
-Brazma, A., Hingamp, P., Quackenbush, J., et al. Minimum information about a
icroarray experiment (MIAME)—towards standards for microarray data. Nature
Genetics 29(12):365–371, 2001. http://www.nature.com/ng/journal/v29/n4/pdf/ng1201365.pdf
Ontologies
-Bodenreider , O., and Stevens, R. Bio-ontologies: current trends and future directions.
Briefings in Bioinformatics 7(3):256–275.
http://bib.oxfordjournals.org/content/7/3/256.short,
-Blake, J.A., and Bult, CJ. Beyond the data deluge: Data integration and bio-ontologies.
Journal of Biomedical Informatics 39:314–320, 2006.
http://www.sciencedirect.com/science/article/pii/S1532046406000190
-Noy, N.F., and McGuinness, D.L., Ontology Development 101: A Guide to Creating Your
First Ontology. http://bmir.stanford.edu/file_asset/index.php/108/SMI-2001-0880.pdf
Challenges with Ontologies
-Rogers, J., and Rector, A. GALEN’s model of parts and wholes: Experience and
comparisons. In: Proc. AMIA Ann Symp 2000:714–718.
http://www.opengalen.org/download/PartsAndWholes.pdf
-Smith, B., Ashburner, M., Rosse, C., et al. The OBO Foundry: coordinated evolution of
ontologies to support biomedical data integration. Nature Biotechnology 25
:1251–1255, 2007. http://www.nature.com/nbt/journal/v25/n11/full/nbt1346.html
Knowledge Representation
-Barski, C. How to tell stuff to a computer. http://www.lisperati.com/tellstuff/index.html
-Davis, R. Shrobe, H., and Szolov its, P. What is a knowledge representation? AI
Magazine 14(1):17–33, 1993. http://groups.csail.mit.edu/medg/ftp/psz/k-rep.html
Rule-based Systems
-Clancy, W.J. The epistemology of a rule-based system: A framework for explanation.
Artificial Intelligence 29:215–251, 1983.
http://cll.stanford.edu/~sborrett/media/Clancey1983_epistemology%20of%20a%20rul
e%20based%20expert%20system.pdf
3/5
Module card - Modeling biomedical systems
Probabilistic Reasoning
-Charniak, E. Bayesian networks without tears. AI Magazine,12(3):50–63, 1991.
http://www-psych.stanford.edu/~jbt/224/Charniak_91.pdf
Description Logic
-Mazzocchi, S. Close World vs. Open World: the First Semantic Web Battle.
http://www.betaversion.org/~stefano/linotype/news/91/
-Rector, A.L. Getting the foot out of the pelvis: modeling problems affecting SNOMED
CT hierarchies in practical applications. Journal of the American Medical Informatics
Association 18:432–440, 2011.
http://jamia.bmj.com/content/18/4/432.full.pdf+html?sid=ed9ab9a6-ce53-461e-86d54c7a27b9f2b0
Representing Tasks and Procedures
Newell, A. The knowledge level. AI Magazine 2(2), Fall 1981.
http://www.aaai.org/ojs/index.php/aimagazine/article/view/99
Developing Systems
Stasys, B. How Siri on iPhone 4S works and why it’s a big deal.
http://bit.ly/unwiredview_Siri-is-a-Big-Deal
Cognitive Biases
-Tversky, A., and Kahneman, D. Judgment under uncertainty: Heuristics and biases.
Science 185:1124–1131, Sept 27, 1974.
http://www.sciencemag.org/content/185/4157/1124.full.pdf
Controversies in Informatics
-Robbins, P., and Aydede, M. A short primer on situated cognition. In: Robbins, P., and
Aydede, M., eds. The Cambridge Handbook of Situated Cognition, 2009.
http://sites.google.com/site/philipmovealpha/short_primer.pdf
-Compton, P., Peters, L., Edwards, G., and Lavers, T.G. Experience with ripple-down
rules. In: Specialist Group on AI Conference, 2005.
http://www.sciweavers.org/publications/experience-ripple-down-rules
-Merrill, G.H. Realism and reference ontologies: Considerations, reflections, and
problems. Applied Ontology 5(3, 4):189–221, 2010.
http://iospress.metapress.com/content/21l17015v46687v0/
Seminar classes
Modeling biomedical systems
Preperation of presentations on the selected lectures, development of practical
computer applications.
Method of calculating the final grade
Final test 50 % + Computational project 35% + Presentation 15%
Prerequisites and additional requirements
Zgodnie z Regulaminem Studiów AGH podstawowym terminem uzyskania zaliczenia jest ostatni dzień
zajęć w danym semestrze. Termin zaliczenia poprawkowego (tryb i warunki ustala prowadzący moduł na
zajęciach początkowych) nie może być późniejszy niż ostatni termin egzaminu w sesji poprawkowej (dla
przedmiotów kończących się egzaminem) lub ostatni dzień trwania semestru (dla przedmiotów
niekończących się egzaminem).
Recommended literature and teaching resources
4/5
Module card - Modeling biomedical systems
Literature listed in the content of lectures.
Scientific publications of module course instructors related to the topic of
the module
http://www.bpp.agh.edu.pl/
Additional information
-
Student workload (ECTS credits balance)
Student activity form
Student workload
Examination or Final test
2h
Contact hours
14 h
Participation in lectures
18 h
Participation in seminar classes
18 h
Realization of independently performed tasks
50 h
Preparation of a report, presentation, written work, etc.
30 h
Summary student workload
132 h
Module ECTS credits
5 ECTS
5/5