Verfügbare Projekte
... für Studierende der Informatik
Es sind aktuell verfügbar (sekundäres Zulassungsverfahren, Stand: 13.11.2024):
- 1 Masterprojekt(e) für Studierende der Informatik.
- 0 Promotionsprojekt(e) für Studierende der Humanmedizin
Bei Interesse an den Projekten sowie bei inhaltlichen Fragen wenden Sie sich bitte direkt an die jeweilige Projektleitung. Die Bewerbung erfolgt über das DigiStrucMed-Programm, bitte lassen Sie uns bei Interesse am Projekt Ihre vollständigen Bewerbungsunterlagen zukommen.
Ein Einstieg in das Projekt ist möglich für Informatikstudierende ab sofort bis 1. Februar 2024 (in Absprache mit den Projektverantwortlichen) und für Medizinstudierende zum 1. Juli oder 1. August 2024 (späterer Beginn ggf. möglich nach Rücksprache).
P10 - Evaluation of the clinical utility of wearable fitness tracking devices to predict liver related outcomes of patients with liver cirrhosis
(Verfügbar: Masterprojekt für Studierende:n der Informatik)
Frailty is a common and serious complication in patients with liver cirrhosis, which has a negative impact on quality of life and the long-term clinical course. Diagnosis of frailty is based on the Liver Frailty Index (LFI), which is a clinical assessment including physical exercises (balancing, hand grip strength and chair raises). Frailty assessment is time and personnel consuming and is therefore not regularly used in clinical routine. Wearable fitness tracking devices (WFT) use accelerometer data to assess activity levels. and are integrated into smartphones and smartwatches. Thus, they offer a longer, more comprehensive and unsupervised data assessment. Our own preliminary data show that accelerometer data correlates with frailty assessment by LFI. Therefore, our hypothesis is that WFTs can replace LFI for frailty assessment and will even be superior in the prediction of liver-related outcomes in patients with cirrhosis. In this project LFI and accelerometer data will be assessed by the medical student who will also collect clinical data and perform follow-up visits including various clinical assessments. Associations between the accelerometer data and the LFI will be investigated jointly by the medical student in close collaboration with the computer science student. To this end, the computer science student will develop a machine learning approach, predicting LFI from WFT data as well as frequent cirrhosis-associated complications. Applicability and transferability to commercially available WFTs will be evaluated by the computer science student.
Projektleitung:
Prof. Dr. med. Benjamin Maasoumy
Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology
Hannover Medical School
Betreuung der Informatik:
Division Data Science in Biomedicine,
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School (TU Braunschweig)