| Dozent/in |
Lecturer and course responsible: PD Dr. med. Patrick Beeler; lecturer and co-examiner: Dr. med. Dr. sc. nat. Michael Havranek; co-examiner: Prof. Dr. med. Balthasar Hug |
| Veranstaltungsart |
Vorlesung |
| Code |
FS261002 |
| Semester |
Frühjahrssemester 2026 |
| Durchführender Fachbereich |
Gesundheitswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Mi, 25.02.2026, 09:15 - 12:00 Uhr, 4.A05
Mi, 04.03.2026, 09:15 - 12:00 Uhr, 4.A05
Mi, 11.03.2026, 09:15 - 12:00 Uhr, 4.A05
Mi, 18.03.2026, 09:15 - 12:00 Uhr, 4.A05
Mi, 25.03.2026, 09:15 - 12:00 Uhr, 4.A05
Mo, 30.03.2026, 14:15 - 18:00 Uhr, 4.B55 (Prüfung)
Mi, 01.04.2026, 09:15 - 12:00 Uhr, 4.A05
Mi, 15.04.2026, 09:15 - 12:00 Uhr, 4.A05
Mi, 22.04.2026, 09:15 - 12:00 Uhr, 4.A05
Mi, 29.04.2026, 09:15 - 12:00 Uhr, 4.A05
Mo, 04.05.2026, 14:15 - 18:00 Uhr, 4.A05
Mo, 11.05.2026, 14:15 - 18:00 Uhr, 4.A05
Mo, 18.05.2026, 14:15 - 18:00 Uhr, 4.A05
Mo, 01.06.2026, 14:15 - 18:00 Uhr, 3.B48 (Prüfung) |
| Umfang |
4 Semesterwochenstunden |
| Inhalt |
In healthcare, increasing amounts of data are routinely collected and stored, driven by digitalization. Such data are called real-world data which the U.S. Food and Drug Administration (FDA) defines as “[…] data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources”. In the research context, real-world evidence results from the analysis of real-world data.
Electronic health records constitute an important real-world data source that collects data during routine clinical practice for patient management and documentation purposes. Electronic health record data can be used to address novel research questions with minimal risks for patients. According to the “Framework for FDA’s Real-World Evidence Program”, real-world evidence may help expand indications for drugs only approved for specific conditions.
Curiosity is an asset in the course “Analysis of Routinely Collected Healthcare Data (ARCHD)”. The students will get the opportunity to exploratively work on anonymized but real patient data routinely collected in electronic health records (e.g. the MIMIC patient datasets). The students will get to know scientific articles based on such data, will practice the handling of large patient datasets, will learn how to process and analyse data and how to apply appropriate statistical methods and machine learning for research purposes.
During this course, the students will generate their own real-world evidence in the form of a capstone project. In the process, they will be guided in posing a research question, preprocessing the data, selecting suitable statistical methods, performing these analyses, and interpreting their findings. Thus, the capstone project will bridge the gap between course work and real-world application. This course will optimally prepare students who are planning to do a quantitative Master’s thesis using real-world data. |
| E-Learning |
To become a credentialed user by following the instructions on https://physionet.org/about/citi-course/ is a prerequisite. This prerequisite includes an e-learning training course before a student gets access to the MIMIC patient datasets.
In this course, the students will work on their own devices (tutorials, exercises, MIMIC patient data analysis). |
| Lernziele |
After having completed this course, you will
- be able to deal with large datasets of real patient data routinely collected in electronic health records
- know how to explore, understand and describe such real-world data, be aware of the advantages and disadvantages of real-world data
- know what techniques are used to process, transform, aggregate and present patient data
- be able to apply the most important statistical methods to generate real-world evidence
- and you will have understood the basic principles and methods of machine learning and are able to apply them |
| Voraussetzungen |
• Course restriction: Participation in this course is limited due to the second practical component, which involves one-on-one student coaching. This format is only feasible with a manageable group size. Interested students must apply by email to patrick.beeler@unilu.ch and michael.havranek@unilu.ch no later than February 17, 2026, at 23:59. Applications must include: 1. statement of motivation (max. 5-7 sentences), 2. (planned) major, and 3. academic transcript (transcript of records) as an attachment. Decisions will be communicated to applicants no later than February 20, 2026 .
• Credentialing requirement: To gain access to the MIMIC patient datasets, students must complete the CITI Program training titled: “Data or Specimens Only Research”. Accepted students must submit both their CITI training report and certificate (2 PDFs) via email to patrick.beeler@unilu.ch no later than February 24, 2026, at 23:59. If the required documents are not submitted by the deadline, students will unfortunately need to be removed from the official course roster and will not be eligible to receive ECTS credits.
The credentialing process is described in detail at: https://physionet.org/about/citi-course/.
• It’s a hands-on course: Bring your own device.
• Each week, the relevant chapter of the ARCHD script will be introduced in class. Students are then expected to work through that chapter independently as homework in the days following the session. This preparation is essential for understanding the key concepts and methods required to successfully complete the course.
Recommended courses:
• Data Modeling and Database Systems
Dr. Ivan Giangreco
• Advanced Quantitative Methods
Prof. Stefan Boes |
| Sprache |
Englisch |
| Begrenzung |
This a core course in the major "Health Data Science" |
| Anmeldung |
Moodle: https://elearning.hsm-unilu.ch/course/view.php?id=1003
|
| Leistungsnachweis |
1) First oral presentation with slides of a scientific article, on March 30, 2026, during course (not graded) (submission of slides on March 29, 2026)
2a) Submission of written abstract on May 31, 2026, on student’s own capstone project developed during the course (not graded*)
2b) Submission of code on May 31, 2026: R or Python code and all or the most significant SQL statements used (not graded*)
2c) Second oral presentation with slides of student’s own capstone project on June 1, 2026, during course (mean of the three examiners’ grades; *abstract and or code may be considered in cases of disagreement between examiners) (submission of slides on May 31, 2026)
IMPORTANT: In order to earn credits and participate at the exam registration via Uni Portal within the exam registration period is MANDATORY. Further information: www.unilu.ch/en/study/courses-exams-regulations/health-sciences-and-medicine/exams/ |
| Abschlussform / Credits |
oral presentations, abstract, code / 6 Credits
|
| Hinweise |
Teaching methods:
Longitudinal course with blended learning, including lectures, tutorials, hands-on exercises and class discussions as well as a supervised capstone project during the second part of the course. |
| Hörer-/innen |
Nein |
| Kontakt |
Lecturer and course responsible: PD Dr. med. Patrick Beeler;
Lecturer and co-examiner: Dr. med. Dr. sc. nat. Michael Havranek;
Co-examiner: Prof. Dr. med. Balthasar Hug
patrick.beeler@unilu.ch / michael.havranek@unilu.ch / balthasar.hug@unilu.ch |
| Material |
The teaching material is based on slides, hands-on exercises in class, selected scientific articles, and online resources. Offline material will be provided via moodle. |
| Literatur |
While slides and selected scientific articles will be presented and discussed,
in this course it will be more important for the students
- to learn and practice working on data,
- to be curious and to explore data, techniques and methods,
- to get to know essential online resources, and
- to learn resolving issues/overcoming obstacles with the help of online research. |