LV 709.049 Biomedical Informatics – Discovering Knowledge in (Big*) Data

Winter Term 2016/17 – class started on October, 12, 2016, 11:15
First exam on 1th Februrary 2017; Second exam in June, Third exam in October 2017
*) and sometimes small amounts of complex data

medical_informaticsThis course covers computer science aspects of biomedical informatics (medical informatics, health informatics, bioinformatics). The focus is on knowledge extraction and discovery from complex data by applied machine learning and concentrating on algorithmic and methodological issues of data science.

The class is taking place from 11:15 to 12:45 in Lecture Hall BMTEG 138, Stremayrgasse 16, EG
Tutor: Markus Plass (Holzinger-Group)

Note: Sample exam questions with solutions can be found in the Springer textbook available at the Library: Andreas Holzinger (2014). Biomedical Informatics: Discovering Knowledge in Big Data, New York: Springer. DOI: 10.1007/978-3-319-04528-3 – however, please always check the latest course material for new stuff.

This course is compulsory for bachelor students of Biomedical Engineering (5th semester), and elective for master students Biomedical Engineering, but also recommendable for Telematics (Subject area catalog: Medical Informatics, Bioinformatics, and Neuroinformatics), Informatics and Software Development. The complete course is presented in English as it is part of the Doctoral School Informatics – so PhD students and international students are very welcome!

Definition: Biomedical Informatics (more generally called Health Informatics, HI) can be defined as an interdisciplinary field that studies and pursues the effective use of biomedical data, information and knowledge for problem solving and decision making, motivated by efforts to improve human health and well-being.

Consequently, this course is focusing on data, information and knowledge in the context of biomedicine, life sciences, health and well-being. The lectures follow a research-based teaching (RBT) style, showing the students state-of-the-art science and engineering, and discussing some underlying fundamentals and basic principles of this extremely important, challenging and future oriented field: towards the approach of personalized, molecular medicine, smart health and on how sophisticated machine learning algorithms and knowledge discovery methods can help. A grand goal of future medicine is in modelling the complexity of patients to tailor medical decisions, health practices and therapies to the indiviudal patient. This trend towards personalized medicine produces unprecedented amounts of data, see A. Holzinger, “Trends in Interactive Knowledge Discovery for Personalized Medicine: Cognitive Science meets Machine Learning“, IEEE Intelligent Informatics Bulletin, vol. 15, iss. 1, pp. 6-14, 2014.

This course will foster an integrated approach: for the successful application of machine learning algorithms in health, a comprehensive and overarching overview of the data science ecosystem and knowledge extraction and discovery pipeline is essential. This means that a multidisciplinary skill set is required, cross-domain, encompassing the following seven specialisations: 1) data science, 2) machine learning algorithms, 3) network science, 4) graphs/topology, 5) time/entropy, 6) data visualization and visual analytics, and last but not least 7) privacy, data protection, safetey and security. See the HCI-KDD approach.

Always remember: Science is to test crazy ideas – Engineering is to put these ideas into Business!

The course 2016 consists of the following 12 lectures:

  1. Introduction: Computer Science meets Life Sciences, challenges and problems;
  2. Back to the future: Fundamentals of biomedical Data, Information and Knowledge, Entropy and Kullback-Leibler Divergence;
  3. Structured Data: Knowledge Representation, Ontologies and Medical Classification;
  4. Decision, Cognition, Uncertainty, Bayesian Statistics and Probabilistic Modelling;
  5. Probabilistic Graphical Models Part 1: From Knowledge Representation to Graph Model Learning;
  6. Probabilistic Graphical Models Part 2: From Bayesian Networks to Graph Bandits;
  7. Dimensionality Reduction and Subspace Clustering with the Doctor-in-the-Loop;
  8. Biomedical Decision Making: Reasoning and Decision Support;
  9. Intelligent, interactive Information Visualization and Visual Analytics;
  10. Biomedical Information Systems and Medical Knowledge Management;
  11. Biomedical Data Protection: Privacy, Safety and Security;
  12. Summary and future challenges in biomedical informatics;
LV 709.049 Schedule Winter Term 2016/17, NOTE: The slides get updated right after each lecture!
DAY
and
DATE
TIMELECTURE
TOPICS
SLIDES
6 per page
SLIDES
full
size
Additional ReadingVIDEO
TU Graz
Mi,
12.10.
2016
11:15 – 12:4501 Introduction and Overview
Computer Science meets Life Sciences
Challenges and Future Directions
[Lecture 01 – 6 Slides per page 1878kB] [Lecture 01 – full size slides 12980kB] [Holzinger, A. 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131][Link to TUbe Video 01]
Mi,
19.10.
2016
11:15 – 12:4502 Back to the Future – Fundamentals of biomedical Data, Information and Knowledge; Entropy and Kullback-Leibler Div.[Lecture 02 – 6 Slides per page 3807kB] [Lecture 02 – full size slides 7432kB] [Holzinger, A., Hörtenhuber, M., Mayer, C., Bachler, M., Wassertheurer, S., Pinho, A. & Koslicki, D. 2014. On Entropy-Based Data Mining. In: LNCS 8401, pp. 209-226, doi:10.1007/978-3-662-43968-5_12][Link to TUbe Video 02]
Mi,
09.11.
2016
11:15 – 12:4503 Structured Data – Knowledge Representation, Ontologies & Medical Classifications[Lecture 03 – 6 Slides per page 4238kB] [Lecture 03 – full size slides 9736kB] [Holzinger, A., Geierhofer, R., Modritscher, F. & Tatzl, R. 2008. Semantic Information in Medical Information Systems: Utilization of Text Mining Techniques to Analyze Medical Diagnoses. Journal of Universal Computer Science, 14, (22), 3781-3795, doi:10.3217/jucs-014-22-3781][Link to TUbe Video 03]
Mi,
16.11.
2016
11:15 – 12:4504 Probability, Uncertainty, Bayesian Statistics, Probabilistic Modelling,
Cognition and Decision Making
[Lecture 04 – 3 x 3 Slides per page 4243kB] [Lecture 04 – full size slides 9469kB] [Link to TUbe Video 04]
Mi,
23.11.
2016
11:15 – 12:4505 Probabilistic Graphical Models Part 1: From Knowledge Representation to Graph Model Learning[Lecture 05 – 3 x 3 Slides per page 4133kB] [Lecture 05 – full size slides 13001kB] [Hund, M., Boehm, D., Sturm, W., Sedlmair, M., Schreck, T., Ullrich, T., Keim, D. A., Majnaric, L. & Holzinger, A. 2016. Visual analytics for concept exploration in subspaces of patient groups: Making sense of complex datasets with the Doctor-in-the-loop. Brain Informatics, 3, (4), 233-247, doi:10.1007/s40708-016-0043-5][Link to TUbe Video 05]
Mi,
30.11.
2016
11:15 – 12:4506 Probabilistic Graphical Models Part 2: From Bayesian Networks to Graph Bandits[Lecture 06 – 3 x 3 Slides per page 3762kB] [Lecture 06 – full size slides 10959kB] [Link to TUbe Video 06]
Mi,
07.12.
2016
11:15 – 12:4507 Dimensionality Reduction and Subspace Clustering with the Doctor-in-the-Loop[Lecture 07 – 3 x 3 Slides per page 3582kB] [Lecture 07 – full size slides 7050kB] [Link to TUbe Video 07]
Mi,
14.12.
2016
11:15 – 12:4508 Biomedical Decision Making: Reasoning and Decision Support[Lecture 08 – 3 x 3 Slides per page 3024kB] [Lecture 08 – full size slides 5552kB] [Link to TUbe Video 08]
Mi,
11.01.
2017
11:15 – 12:4509 Intelligent, Interactive Visualization and Visual Analytics[Lecture 09 – 3 x 3 Slides per page 4260kB] [Lecture 09 – full size slides 12571kB] [Link to TUbe Video 08]
Mi,
18.01.
2017
11:15 – 12:4510 Biomedical Information Systems and Medical Knowledge Management[Lecture 10 – 3 x 3 Slides per page 3369kB] [Lecture 10 – full size slides 12633kB] [Link to TUbe Video 08]
Mi,
25.01.
2017
11:15 – 12:4511 Biomedical Data Protection, Privacy, Safety and Security
plus SUMMARY of the whole course
[Lecture 11 – 3 x 3 Slides per page 3954kB] [Lecture 11 – full size slides 6899kB]
Mi,
01.02.
2017
exam
11:15 – 12:4512 Summary Reflection Lecture[Lecture 12 – full size slides 5307kB]

Please NOTE: Each year this course will be updated. Old lecture slides from last year can be found below, however, for the next exam only the most recent one are relevant.

LV 709.049 Schedule Winter Term 2015/16
DAY
and
DATE
TIMELECTURE
TOPICS
SLIDES
6 per page
SLIDES
with
notes
SLIDES
full
size
VIDEO
TU Graz
Mi, 14.10.201511:15 – 12:4501 Intoduction and Overview: Computer Science meets Life Sciences, challenges and future directions

PDF IconPDF IconPDF IconVideo Icon
Mi, 21.10.201511:15 – 12:4502 Back to the Future – Fundamentals of biomedical Data, Information and KnowledgePDF IconPDF IconPDF IconVideo Icon
Mi, 28.10.201511:15 – 12:4503 Structured Data – Coding, Classification (ICD, SNOMED, MesH, UMLS & Co)PDF IconPDF IconPDF IconVideo Icon
Mi, 04.11.201511:15 – 12:4504 Biomedical Databases: Data Acquisition, Storage, Information Retrieval and UsePDF IconPDF IconPDF IconVideo Icon
Mi, 11.11.201511:15 – 12:4505 Semi Structured, weakly structured data; Graphs, Networks and HomologiesPDF IconPDF IconPDF IconVideo Icon
Mi, 18.11.201511:15 – 12:4506 Multimedia Data Mining and Knowledge DiscoveryPDF IconPDF IconPDF IconVideo Icon
Mi, 25.11.201511:15 – 12:4507 Knowledge, Decision, Cognition, Probability, Uncertainty, Bayesian Statistics, Probabilistic ModellingPDF IconPDF IconPDF IconVideo Icon
Mi, 02.12.201511:15 – 12:4508 Biomedical Decision Making: Reasoning and Decision SupportPDF IconPDF IconPDF IconVideo Icon
Mi, 09.12. 201511:15 – 12:4509 Intelligent, Interactive Visualization and Visual AnalyticsPDF IconPDF IconPDF IconVideo Icon
Mi, 16.12.201511:15 – 12:4510 Biomedical Information Systems and Medical Knowledge ManagementPDF IconPDF IconPDF Icon
Mi, 13.01.201611:15 – 12:4511 Biomedical Data Protection, Privacy, Safety and SecurityPDF IconPDF IconPDF IconVideo Icon
Mi, 20.01.201611:15 – 12:4512 Methodology for Information Systems: Usability, Quality, Benchmarking and EvaluationPDF IconPDF IconPDF Icon

The course 2015 consisted of the following 12 lectures:

  1. Introduction: Computer Science meets Life Sciences, challenges and future directions;
  2. Back to the future: Fundamentals of biomedical Data, Information and Knowledge;
  3. Structured Data: Coding, Classification (ICD, SNOMED, MeSH, UMLS);
  4. Biomedical Databases: Acquisition, Storage, Information Retrieval and Use;
  5. Semi structured and weakly structured data (structural homologies);
  6. Multimedia Data Mining and Knowledge Discovery;
  7. Knowledge, Decision, Cognition, Probability, Uncertainty, Bayesian Statistics, Probabilistic Modelling;
  8. Biomedical Decision Making: Reasoning and Decision Support;
  9. Interactive Information Visualization and Visual Analytics;
  10. Biomedical Information Systems and Medical Knowledge Management;
  11. Biomedical Data Protection: Privacy, Safety and Security;
  12. Methodology for Information Systems: Systems Design, Usability and Evaluation;

Lecture Slides from previous courses are available here:
[Lecture LV 444.152 MEDICAL INFORMATICS [2 VO WS] at the Institute of Genomics and Bioinformatics]
[Lecture LV 444.152 MEDICAL INFORMATICS [2 VO WS] at itunes TU Graz]

Exam Example is available here:
Exam Example LV 444.152 MEDICAL INFORMATICS [2 VO WS]

Springer Textbook Discovering Knowledge in Big Data

Springer Textbook available to this course

The course corresponds to the NEW Springer Student Textbook (available via the TU Bibilothek):
Biomedical Informatics – Discovering Knowledge in Big Data (Paperpack)
Biomedical Informatics – Discovering Knowledge in Big Data (Kindle Edition)

A more comprehensive book is the previous version (also available via the TU Bibilothek):
Biomedical Informatics: Computational Sciences meets Life Sciences (Paperpack)
Biomedical Informatics: Computational Sciences meets Life Sciences (Kindle Edition)

The life sciences, biomedicine and health are increasingly turning into a data science, where we face not only increased volumes and a diversity of highly complex, multi-dimensional and often weakly-structured and noisy data, but also the growing need for integrative machine learning approaches. Automatic Machine Learning (aML) can be of great help here, particularly when having big data sets – where algorithms can learn from it. However, sometimes we deal not with big data, but with complex data, rare events or even NP-hard problems, e.g. in subspace clustering, protein folding, or k-Anoynmization, where such aML-approaches fail or at least carries the danger of modelling artefacts. In such situations it is benefical to make use of interactive Machine Learning (iML) by putting the doctor-into-the-loop of the machine learning algorithms.