Health Data Science | MaD Lab

The Health Data Science group is dedicated to advancing the personalization of care and therapeutics through the design and application of cutting-edge, high-performance machine learning algorithms and methods. Our team integrates interdisciplinary expertise in advanced machine learning, big data analytics, learning-based control theory and biomedical engineering to tackle some of the most challenging topics in personalized medicine. By translating complex electronic medical records, laboratory test results and sensor-based health data into actionable insights and supporting evidence-based decision making, we develop novel, clinically safe, highly scalable predictive models (e.g., federated virtual twins), intelligent clinical decision support systems and closed-loop therapeutical systems. The ultimate objective is to derive and employ reliable, intelligent computational methods essential for delivering medical treatments tailored to individual patients. The impact of our work is reflected through our strong international collaborations with industrial and clinical partners as well as participation in large-scale EU-funded research projects.

Group Head

Dr. Alexandros Tanzanakis

Room 01.024

Email: alexandros.tanzanakis@fau.de

Group Members

Madeleine Flaucher, M. Sc.

Mahdis Habibpour, M. Sc.

Tatiana Merzhevich, M. A.

Thanh Truc Tran, M. Sc.

Students

If you are interested in writing a Bachelor’s or Master’s thesis in our group, please check the lab’s Student Theses and Jobs.

Projects

2024

Altmannshofer S., Flaucher M., Beierlein M., Eskofier B., Beckmann M., Fasching P., Hübner H.:
A content-based review of mobile health applications for breast cancer prevention and education: Characteristics, quality and functionality analysis
In: Digital Health 10 (2024)
ISSN: 2055-2076
DOI: 10.1177/20552076241234627
BibTeX: Download
Flaucher M., Prümer F., Jäger K., Rolny J., Trissler P., Eckl S., Eskofier B., Leutheuser H.:
Motivational Factors for Experienced Users of Mobile Health Applications in Heart Failure Management
2024 Nordic Conference on Human-Computer Interaction (Uppsala, 13. October 2024 - 16. October 2024)
In: NordiCHI '24 Adjunct: Adjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction, New York City: 2024
DOI: 10.1145/3677045.3685447
BibTeX: Download
Moradi H., Flaucher M., Ott T., Ringgold V., Eskofier B., Leutheuser H.:
Can HCI Lead the Way? Workshop on Exploring Conscious AI
In: Mensch und Computer - Workshopband 2024, Gesellschaft für Informatik e.V., 2024
DOI: 10.18420/muc2024-mci-ws12-113
BibTeX: Download
(Conference report)
Nissen M., Huang SY., Jäger K., Flaucher M., Titzmann A., Bleher H., Pontones C., Hübner H., Danzberger N., Fasching P., Eskofier B., Leutheuser H.:
Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception
In: BMC Pregnancy and Childbirth 24 (2024), Article No.: 782
ISSN: 1471-2393
DOI: 10.1186/s12884-024-06959-1
BibTeX: Download

2023

Flaucher M., Zakreuskaya A., Jäger K., Richer R., Smeddinck JD., Kumar D., Grimme S., Klein J., Hrynyschyn R., Eskofier B., Leutheuser H.:
Your Health, Your Data: Combining Interdisciplinary Views, Concepts, and Practices to Empower Patients in Their Engagement With Personal Health Data
Mensch und Computer 2023 (Rapperswil, 3. September 2023 - 6. September 2023)
In: Workshopband 2023
DOI: 10.18420/muc2023-mci-ws14-117
BibTeX: Download
Flaucher M., Zakreuskaya A., Nissen M., Mocker A., Fasching P., Beckmann M., Eskofier B., Leutheuser H.:
Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review
In: Oncologist (2023)
ISSN: 1083-7159
DOI: 10.1093/oncolo/oyad217
BibTeX: Download
Nissen M., Barrios Campo N., Flaucher M., Jäger K., Titzmann A., Blunck D., Fasching P., Engelhardt V., Eskofier B., Leutheuser H.:
Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data
In: npj Digital Medicine 6 (2023), Article No.: 189
ISSN: 2398-6352
DOI: 10.1038/s41746-023-00935-3
BibTeX: Download
Nissen M., Flaucher M., Jäger K., Hübner H., Danzberger N., Titzmann A., Pontones C., Fasching P., Eskofier B., Leutheuser H.:
WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography
45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 (Sydney, NSW, 24. July 2023 - 27. July 2023)
In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2023
DOI: 10.1109/EMBC40787.2023.10340204
BibTeX: Download
Nissen M., Perez CA., Jäger K., Bleher H., Flaucher M., Hübner H., Danzberger N., Titzmann A., Pontones C., Fasching P., Beckmann M., Eskofier B., Leutheuser H.:
Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study
In: JMIR Pediatrics and Parenting 6 (2023), Article No.: e50765
ISSN: 2561-6722
DOI: 10.2196/50765
BibTeX: Download

2022

Flaucher M., Nissen M., Jäger K., Titzmann A., Pontones C., Hübner H., Fasching P., Beckmann M., Gradl S., Eskofier B.:
Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care
In: IEEE Journal of Translational Engineering in Health and Medicine (2022), p. 1-9
ISSN: 2168-2372
DOI: 10.1109/JTEHM.2022.3179147
BibTeX: Download
Nissen M., Slim S., Jäger K., Flaucher M., Hübner H., Danzberger N., Fasching P., Beckmann M., Gradl S., Eskofier B.:
Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study
In: JMIR Formative Research 6 (2022), p. e33635
ISSN: 2561-326X
DOI: 10.2196/33635
BibTeX: Download

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