Medical Data Science

Description:

​Welcome to the Department for Medical Data Sciences at the University Medical Center in Leipzig. We are an interdisciplinary team focusing on medical data sciences in order to increase the value for the patients. In this way, we are mainly working with medical data. Our focus is twofold. On the one hand, we are working on different facets of computing infrastructures allowing us to prepare and analyze medical data, such as for distributed computing. Secondly, we focus on the analysis of medical data itself. We apply classical statistical methods but also machine and deep learning methods known from the artificial intelligence area.

We are open for new projects and topics related to medical data sciences. Please feel free to contact us in case you have any questions or you are interested in collaborating with us.

Web page: https://www.uniklinikum-leipzig.de/einrichtungen/medical-data-science/en/team

Funding details:
No funding details specified

Related items

LEUKO-EXPERT

Rare diseases (RD) occur with a prevalence of less than 1:2,000 persons (EU definition). More than 8,000 RD are known, often genetically determined. Due to their rare occurrence, the RD are often not diagnosed at all, incorrectly or with a long delay diagnosed. Medical experts are mainly involved in selected Centers for Rare Diseases available.The goal of the LEUKO-Expert project is to establish an expert system for the diagnostic support of RD Leukodystrophy and to evaluate it within the scope ...

Programme: Medical Data Science

Public web page: https://leukoexpert.hs-mittweida.de/de/home-deutsch/

Start date: 1st Oct 2020

End date: 10th Feb 2024

LeukoExpert
No description specified

Programme: Medical Data Science

Public web page: https://leukoexpert.hs-mittweida.de/

Start date: 1st Oct 2020

End date: 10th Feb 2024

Prediction model of Length of Stay and probability of rehospitalization
No description specified

Programme: Medical Data Science

Public web page: Not specified

Synthetica: generation and evaluation of synthetic data

Generating and evaluating synthetic data in the medical domain. The synthetic data could be tabular or image.

Programme: Medical Data Science

Public web page: Not specified

Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
Prediction model of Length of Stay and probability of rehospitalization

Abstract (Expand)

Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit (ICU) in relation to their health status helps healthcare management allocate appropriate resources and … resources and better plan for the future. This paper presents predictive models for the LOS of ICU patients from the MIMIC-IV database based on typical demographic and administrative data, as well as early vital signs and laboratory measurements collected on the first day of ICU stay. The goal of this study was to demonstrate a practical, stepwise approach to predicting patient’s LOS in the ICU using machine learning and early available typical clinical data. The results show that this approach significantly improves the performance of models for predicting actual LOS in a pragmatic framework that includes only data with short stays predetermined by a prior classification.

Authors: Lars Hempel, Sina Sadeghi, Toralf Kirsten

Date Published: 1st Jun 2023

Publication Type: Journal article

GAN-Based Approaches for Generating Structured Data in the Medical Domain
Synthetica: generation and evaluation of synthetic data

Abstract (Expand)

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations …ng limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

Authors: Masoud Abedi, Lars Hempel, Sina Sadeghi, Toralf Kirsten

Date Published: 1st Jul 2022

Publication Type: Journal article

Questionnaires RedCAP LeukoExpert
LEUKO-EXPERT

There are 5 questionnaires for the LeukoExpert Project:

  • basic data
  • medical history
  • examination sheet
  • mri
  • genetics

Creators: None

Submitter: Lars Hempel

Data file type: Clinical Data

Models for the structured data
LEUKO-EXPERT
No description specified

Creator: Lars Hempel

Submitter: Lars Hempel

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The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

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