Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI
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Data Management for Digital Health (Wintersemester 2022/2023)

Lecturer: Dr.-Ing. Matthieu-Patrick Schapranow (Digital Health - Personalized Medicine)
Tutors: M.Sc. Florian Borchert Dr.-Ing. Mozhgan Bayat M.Sc. Aadil Rasheet
Course Website: https://hpi.de/en/digital-health-center/teaching/winter-term-2022-23/data-management-for-digital-health.html

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.2022 - 31.10.2022
  • Examination time §9 (4) BAMA-O: 13.02.2023
  • Teaching Form: Lecture / Exercise
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English

Programs, Module Groups & Modules

IT-Systems Engineering MA
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-K Konzepte und Methoden
Data Engineering MA
Digital Health MA
Software Systems Engineering MA

Description

Welcome to the class: We are very excited that you are interested in learning more about the foundations of data management for digital health. In this lecture, we will provide you specific examples from the field of digital health to understand where and how data is acquired, which challenges are specific for these types of data and how to address them, and how to benefit from the analysis of high-dimenstional digital health data with latest latest technology advances, such as machine learning.

After participating in the course, you will be equipped with the ability to: 

  • Assess requirements of selected real-world use cases from the medical field (oncology, nephrology, infectious diseases),
  • Select latest technology building blocks to create viable healthcare software solutions, and
  • Analyze requirements for data analysis and processing, e.g. for machine learning (supervised and unsupervised learning, natual language processing, image analysis).

In the course, we will have invited guest speakers sharing their real-world experience with you in interactive presentation. Thus, you will have the chance to raise any questions you never dared ask and discuss them together with us in the course of the lecture.

Further details about the structure of the lecture will be shared with you in kickoff lecture

Please check the course website for further details.

Requirements

The lecture is a designed to be a foundation lecture for all backgrounds.  

Examination

In the course of the lecture, you will have to conduct a small number of personal exercises to recap the presented lecture content. You have to pass all of these intermediate exercises prior to participate the final exam (Prüfungsvorleistung).

The final grading will be determined by the following parts:

  • Final exam at the end of the course (100%).

Dates

  • Thu Oct 20, 2022: HPI Campus I, room: H-2.57/58 -- due to the high number of interested students, the room for kick off lecture has changed!  
  • Schedule:
    • Mondays @ 11.00am (s.t.)
    • Thursdays @ 11.00am (s.t.)
  • First course:
    • Thu Oct 20, 2022 @ 11.00am (s.t.)

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