Classic vs. Deep Learning-based Time Series Forecasting [Master's Thesis - Computer Science (or similar)]

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Classic vs. Deep Learning-based Time Series


Master’s Thesis - Computer Science (or similar)

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Clinical care and research heavily rely on the documentation of developments over time concerning patients’

health conditions and capacity planning. Thus, many so-called time series data are collected every day in

the medical domain. A well-known example is the chart of vital parameters like heart activity, oxygen saturation

etc. To improve patient care and cost efficiency, the prediction of future values is of great interest.

Your task will be to compare a new forecasting mechanism based on Deep Learning against existing classic

statistic approaches. The project has two main parts, the development/implementation of an artificial

neural network and the realisation of a profound comparison system. You do not need any previous medical

knowledge but should be interested in this domain. The thesis can be written in English or German

(depending on your examination regulations). The project will be supervised by the “Medical Informatics”

group of the Institute of Medical Statistics, Computer and Data Sciences that belongs to the Jena University

Hospital. If you are interested in this project or similar ones, do not hesitate to get in touch or follow us on

Twitter (@imsid_mi).


Sven Festag

Institute of Medical Statistics, Computer and Data Sciences (IMSID)

Jena University Hospital

Bachstraße 18, 07743 Jena

sven.festag@med.uni-jena.de (German or English)

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