Healthcare

Project Overview

Here you will find a number of projects we have realized in the field of Healthcare. Use the ‘+’-sign to see additional information.

 

Start date: 01.03.2016

 

Funded by: University of Augsburg

 

Local head of projectJulia Rauscher

 

 

Abstract

Medical Information Sciences as an aspect of the interdisciplinary integration of medical, biological and genetic information with special consideration of environmental influences for human health and the course of disease using Internet of Things of medical devices.

Start date: 01.09.2011

 

Funded by: University of Augsburg

 

Local head of project: Heiner Oberkampf

 

 

Dissertation:

Start date: 01.03.2016

 

Funded by: Universität Augsburg

 

Local head of projectJulia Rauscher

 

 

Abstract

Through cooperation between the Central Hospital of Augsburg and the University of Augsburg, common characteristics of long-term survivors with stomach pancreatic tumors are identified. This is intended to create a basis of offering optimal treatment in the future and thus sustainably improving the quality of life.

Start date: 01.09.2016

 

Funded by: BMBF (Federal Ministry of Education and Research)

 

Local head of project:

Bernhard Bauer

Melanie Langermeier

Fabian Rabe

 

 

Abstract

Within the funding concept “Medical Informatics” of the Federal Ministry of Education and Research (BMBF), the University of Augsburg is a partner in the DIFUTURE consortium (The Munich-Tübigen Alliance for Data Integration and Future Medicine). Further consortium partners are the Technical University of Munich, the Ludwig-Maximilian University of Munich and the Eberhard Karls University in Tübigen. The goal of this project is to improve research opportunities and patient care through the exchange and shared use of clinical and research data across locations and institutions. 

 

 

DIFUTURE Website

Start date: 01.01.2020

 

Funded by: BMBF (Federal Ministry of Education and Research)

 

Local head of project:

Fabian Rabe

Fabian Stieler

 

 

Abstract

For years, an increasing digitization in medicine has led to an increasing amount of data. Much of this data is used for the diagnosis of diseases. Based on existing labeled data, artificial intelligence could make predictions or diagnostic suggestions for new data. However, for reliable prediction, especially using Deep Learning, a sufficiently large base of annotated data is needed, which also contains rare diagnoses in enough quantity. This is either very time- and cost-intensive or leads to the fact that existing data cannot be used.

 

The LIFEDATA project will create an open-source framework to address this problem. The scientific concept combines Active Learning (AL) with Deep Neural Networks (DNN) to autonomously select data points with the greatest information value (e.g. rare diagnoses). Human experts annotate these and thus enabling efficient training of machine learning models. Additionally, instead of manually labeling all data points in a dataset, semi-supervised learning combined with AL can generate many annotations semi-automatically.

 

Two use cases from the life sciences that have been studied in the project investigate the added value for different problems and data types. Algorithms to explain the model classifications make the decisions of the trained ML models more comprehensible.

 

To achieve these goals, the AL part of the framework is first implemented and evaluated through a use case with an already annotated medical image dataset. To validate the usability, universality and applicability of the annotation tool, a second use case with ECG data will be realized. Thus, in the synergy of growing data sets and technological innovation, the results of the project can lead to an improvement in patient care.

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