SS 2022: seminar
The lecture is followed by a seminar (LSF). Topics are individually assigned, based on experience and preferences of the students. Group projects of 2 or 3 people are possible. Seminar projects include hands-on implementations and combine mathematical techniques from the lectures with clinical decision support use cases. They can be combined with a Hiwi job or a follow-up thesis.The format of the seminar is based on group work and three presentations distributed over the semester. Each group presents the project tasks at the beginning, first results at the middle, and final overview at the end of the semester:
- Monday, May 2, 12h15 (Introduce topic and work plan)
- Tuesday, June 7, 9h15 (First results and to dos)
- Tuesday, June 28, 9h15 (Final presentation)
We will continue using the MatterMost group of the lecture. I advise you to register directly if you have not done so using this link. Please communicate in the group if you want to join the seminar.
WS 2021: lecture
The lecture (LSF) will be taught in English and addresses Master and PhD students in Mathematics or related fields. There is a short movie as a teaser for the lecture.
| Date | Chapter | Chapter Name | Online? | External Resources | |
|---|---|---|---|---|---|
| 13.10.21 | 1 | Introduction to Clinical Decision Support | MCDS_01_Introduction | ||
| 20.10.21 | 2 | Ordinary Differential Equations | MCDS_02_ODEs | ||
| 20.10.21 | 3.1 | Growth Models | MCDS_03_01_GrowthModels | MED, ytRosling | |
| 27.10.21 | 3.2 | Oscillatory Systems | MCDS_03_02{a,b}_Oscillations | ||
| 3.11.21 | 3.3 | Epidemic Models | MCDS_03_03_SIR | ||
| 3.11.21 | 3.4 | Source Detection for Extrasystoles | MCDS_03_04_Extrasystoles | ooECG | |
| 3.11.21 | 3.5 | Wolff-Parkinson-White Syndrome | MCDS_03_05_WPW | ooWPW | |
| 3.11.21 | 3.6 | Ventricular Assist Devices for Heart Failure | MCDS_03_06_LVAD | ooHF | |
| 10.11.21 | 3.7 | Decoding Complex Cardiac Arrhythmia | MCDS_03_07_Decoding | ooAFib | |
| 10.11.21 | 3.8 | Simple Tumor Models | MCDS_03_08_SimpleTumor | tedAngiogenesis, ooImmune | |
| 17.11.21 | 3.9 | Acute Myeloid Leukemia | MCDS_03_09_AML | ooAML | |
| 24.11.21 | 3.10 | Acute Lymphoblastic Leukemia | MCDS_03_10_ALL | ||
| 24.11.21 | 3.11 | Polycythaemia Vera | MCDS_03_11_PV | ooPV | |
| 1.12.21 | 4 | Analysis of ODE system models | MCDS_04_Analysis | ||
| 15.12.21 | 5 | Simulation | MCDS_05_Simulation | ||
| 5.01.22 | 6.1 | Parameter Estimation: Intro | MCDS_06_01_ParameterEstimation_Intro | ||
| 5.01.22 | 6.2 | Parameter Estimation: Gauss-Newton | MCDS_06_02_ParameterEstimation_GaussNewton | ||
| 12.01.22 | 6 | Parameter Estimation: Case Studies | MCDS_06_03_ParameterEstimation_UseCases | ||
| 19.01.22 | 7.0 | Optimization and Optimal Control: Optimization | MCDS_07_00_Optimization | ||
| 19.01.22 | 7.1 | Optimization and Optimal Control: Optimal Control | MCDS_07_01_OptimalControl | ||
| 19.01.22 | 7.2 | Optimization and Optimal Control: Mixed-Integer Optimal Control | MCDS_07_02_MIOC | ||
| 19.01.22 | 7.3 | Optimization and Optimal Control: Simple Tumor Model | MCDS_07_02_SimpleTumor | ||
| 19.01.22 | 7.4 | Optimization and Optimal Control: Calcium Control | MCDS_07_04_Calcium | ||
| 26.01.22 | 7.5 | Optimization and Optimal Control: SIR | MCDS_07_05_SIR | ||
| 26.01.22 | 7.6 | Optimization and Optimal Control: LVAD | MCDS_07_06_LVAD | ||
| 26.01.22 | 7.7 | Optimization and Optimal Control: Acute Myeloid Leukemia | MCDS_07_07_AML | ||
| 26.01.22 | 7.8 | Optimization and Optimal Control: Polycythemia vera | MCDS_07_08_PV | ||
| 26.01.22 | 8.1 | UQ and Optimal Experimental Design: Uncertainty Quantification | MCDS_08_01_UQ | ||
| 26.01.22 | 8.2 | UQ and Optimal Experimental Design: Optimal Experimental Design | MCDS_08_02_OED | ||
| 26.01.22 | 8.3 | UQ and Optimal Experimental Design: Lotka Volterra | MCDS_08_03_Lotka | ||
| 26.01.22 | 8.4 | UQ and Optimal Experimental Design: Acute Myeloid Leukemia | MCDS_08_04_AML | ||
| 26.01.22 | 8.5 | UQ and Optimal Experimental Design: Extrasystoles | MCDS_08_05_Extrasystoles | ||
| 26.01.22 | 9 | Machine Learning | MCDS_09_00_MachineLearning | ||
| 26.01.22 | 9.1 | Machine Learning: Acute Leukemias | MCDS_09_01_AcuteLeukemias | ||
| 26.01.22 | 9.2 | Machine Learning: Cardiac Arrhytmia | MCDS_09_02_HEAT |
The dates specify the day when the asynchronous content is discussed in presence at 9h15 in G02-106 or via zoom. External resources are links to complementary background, in most cases youtube videos of the Open Osmosis (oo) channel.
Technical setup, Mattermost registration, and (virtual) meetings
The content of the lecture will be provided asynchronously via videos and a lecture script on this password-protected page. Exercises and discussion rounds may either be virtual via zoom (as a password please use the abbreviation (mcds) of this lecture) or in presence. Typically, on Wednesday at 9h15 I will open the zoom session with a notebook in the lecture room G02-106, so you can decide yourself which way of participation you prefer. Note that the lecture time slots have been reallocated compared to the lsf entries after discussion at the first meeting to Wednesday, 9h-11h for the lecture and Wednesday, 11h-13h for the exercises and free time for the asynchronous content.
In addition to asynchronous content and regular meetings, there is also a MatterMost group dedicated to this lecture. I advise you to register directly and before the start of the lecture using this link.
Asynchronous Content, Exercises, and Downloads
On this password-protected page
Rechtlicher Hinweis: Die Vorlesungen und Vorlesungsvideos sind nichtöffentliche Dokumente und ausschließlich Teilnehmerinnen und Teilnehmern der Vorlesung nach vorheriger Anmeldung zugänglich. Alle Rechte liegen bei Prof. Dr. Sebastian Sager sowie beim Institut für Mathematische Optimierung. Jede öffentliche oder private Weitergabe oder Verbreitung der Videos, insbesondere das zur Verfügung stellen über YouTube und andere Plattformen, ist ausdrücklich untersagt. Achtung: auch einige Materialien sind urheberrechtlich geschützt und sollen nur für die Vorlesung genutzt, auf keinen Fall weitergegeben werden.
Information
We will use a hybrid or virtual format, with videos of the lectures that can be asynchronously assessed. Discussions and exercises take either place in presence or using zoom.
- Lecture with 4+2 SWS and 9 ECTS-Credits
- Time slot: Wed 9:15 - 10:45 for discussions concerning the lecture, via zoom or room G02-106
- Time slot: Wed 11:15 - 12:45 for practical exercises, room G02-106, via zoom or room G02-106
- Lecturers and practical exercises
Content
It will survey mathematical concepts for clinical decision support systems and apply them to models of biological/medical problems. We shall also address medical content for non-specialists (mathematicians).
Practical exercises will complement the lecture.
Requirements
Mathematical basics (Analysis and Linear Algebra) and programming skills.
Module description
The lecture can be included in several curriculae, as listed on the LSF page. In particular, it is a master lecture in the mathematics curriculum and described in the module handbook as a Wahlpflicht module:- WPF MA (Module 12, 13, 14)
- WPF MA; M 1-3 (Module M3D)
A translation of the module description:
- Goals and competences: The students acquire competences with respect to modeling and algorithmically solving simulation and optimization problems that arise from the modeling of biological and medical topics. The results shall help to support clinical decision making. Examples are automatic classifications of cardiac arrhytmia, efficient strategies for finding a good ablation point, estimating the course of chemotherapy treatment, and a scheduling of treatments for polycythemia vera.
- Content: In the lecture mathematical techniques are introduced, explained, and applied to concrete examples. This comprehends modeling, simulation, sensitivity analysis, parameter estimation, identifiability, experimental design, optimization, optimal control, dual control, and machine learning. The medical applications are mostly from cardiology and oncology.
The lecture is also open to other master and PhD students of OVGU. However, please note that the lecture is addressed to mathematical master students and assumes a good understanding of mathematical basics.
Questions?
Feel free to pose general questions and let me know if you plan to attend the lecture in our MatterMost channel.