Implementation and Application of Clinical Data Warehousing for Studies in Patients with Heart Failure

Event Details
Date: 23.01.2023, 17:30 o'clock - 18:30 o'clock 
Location: N2045, Universitätsstraße 1, 86159 Augsburg
Organizer(s): Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics
Topics: Informatik, Gesundheit und Medizin
Series of events: Medical Information Sciences
Event Type: Vortrag
Speaker(s): Dr. Mathias Kaspar
BIOINF ASFDASDF DSFASF ASDF ASDF © University of Augsburg

Dr. Mathias Kaspar is group leader of the "SAFICU” junior group at University Hospital and University of Augsburg. He studied applied computer science with a specialization in medical informatics at the University of Göttingen, where he also received his PhD. Prior to his PhD, Dr. Kaspar worked for Siemens Healthcare Solutions in Malvern (PA, USA) and Erlangen (Germany) on patient record systems.


Heart Failure (HF) is a complex clinical syndrome including various co-morbidities. Conducting studies in HF is more often focusing on the documentation of clinical data in increasing detail. Acquiring such data manually, however, is time consuming and thus expensive. This presentation will focus on the technical realization required for the comprehensive data and sample acquisition of a large, single-center HF project – the Acute Heart Failure Registry – conducted at the Comprehensive Heart Failure Center Würzburg. This project includes the application of the local clinical datawarehouse, correct detection of patients with HF in the hospital, information extraction from echocardiographic reports, and image data extraction from the hospital's production PACS.

Dr. Mathias Kaspar is group leader of the "SAFICU” junior group at University Hospital and University of Augsburg. He studied applied computer science with a specialization in medical informatics at the University of Göttingen, where he also received his PhD. Prior to his PhD, Dr. Kaspar worked for Siemens Healthcare Solutions in Malvern (PA, USA) and Erlangen (Germany) on patient record systems. During his PhD, Dr. Kaspar worked for about 2 years at the Computation Institute of the University of Chicago and NorthShore University HealthSystems in Chicago and Evanston (IL, USA) as a PhD guest student on shared visualization and grid computing. Dr. Kaspar worked for about 8 years with the Comprehensive Heart Failure Center in Würzburg on biobanking and clinical datawarehousing. His main interest is in the question of getting the right data from clinical systems, or information contained therein, to the medical researcher using a variety of methods.

More events of this series of events "Medical Information Sciences"

More events: Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics

  • November 2024
  • November 2024 / December 2024
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 01
  • December 2024
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
  • December 2024
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
  • December 2024 / January 2025
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
  • January 2025
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
  • January 2025 / February 2025
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
  • February 2025
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
  • February 2025 / March 2025
    • 24
    • 25
    • 26
    • 27
    • 28
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
  • March 2025
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
  • March 2025 / April 2025
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
  • April 2025
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
  • April 2025 / May 2025
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 01
    • 02
    • 03
    • 04
  • May 2025
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
  • May 2025 / June 2025
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
  • June 2025
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
  • June 2025
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
  • June 2025 / July 2025
    • 30
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
  • July 2025
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
  • July 2025 / August 2025
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
    • 08
    • 09
    • 10
  • August 2025
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
  • August 2025 / September 2025
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02
    • 03
    • 04
    • 05
    • 06
    • 07
  • September 2025
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
  • September 2025 / October 2025
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 01
    • 02
    • 03
    • 04
    • 05
  • October 2025
    • 06
    • 07
    • 08
    • 09
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
  • October 2025 / November 2025
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 01
    • 02

Search