Holger Fröhlich

Head of AI & Data Science Group, Deputy Head of Department

Germany

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About

Holger Fröhlich is the Head of the AI & Data Science Group and Deputy Head of Department at Fraunhofer SCAI in Germany. His research areas encompass the application of artificial intelligence in healthcare, particularly focusing on the early detection and monitoring of diseases such as COVID-19 and Parkinson’s disease. He has contributed to various recent studies aimed at leveraging machine learning for improving health technology tools and data analysis in the context of public health challenges.

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Collaborators (8)

Franz Marxreiter

Assistant Professor

University Hospital Erlangen

GERMANY

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Fabian Prasser

Full Professor

Berlin Institute of Health at Charité - Universitätsmedizin Berlin

GERMANY

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Lisa Kühnel

Pharmaceuticals and Medical Devices Agency

GERMANY

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Jonas Radermacher (born Botz)

Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI

GERMANY

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Moritz Ernst

Deputy Head of working group

University Hospital

GERMANY

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Erzsébet Horváth-Puhó

Aarhus University

DENMARK

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Ulrich Jaehde

University of Bonn

GERMANY

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Tamara Raschka

GERMANY

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Holger Fröhlich

Research Interests

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About

Articles (20)

Wastewater as an early indicator for short-term forecasting COVID-19 hospitalization in Germany

Objective monitoring of motor symptom severity and their progression in Parkinson’s disease using a digital gait device

Recommendations for Successful Development and Implementation of Digital Health Technology Tools

Predicting dementia in people with Parkinson’s disease

Reality Check: The Aspirations of the European Health Data Space Amidst Challenges in Decentralized Data Analysis

Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations

Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning

The EU project Real4Reg: unlocking real-world data with AI

RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer’s disease

On the Fidelity versus Privacy and Utility Trade-Off of Synthetic Patient Data

Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease

Regulatory considerations for developing remote measurement technologies for Alzheimer’s disease research

A dynamic ensemble model for short-term forecasting in pandemic situations

Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review

Transformer models in biomedicine

Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis results

Unraveling progression subtypes in people with Huntington’s disease

Progression subtypes in Parkinson’s disease identified by a data-driven multi cohort analysis

Towards early diagnosis of Alzheimer’s disease: advances in immune-related blood biomarkers and computational approaches

Foresight—generative pretrained transformer for the prediction of patient timelines

Collaborators (8)

Franz Marxreiter

Fabian Prasser

Lisa Kühnel

Jonas Radermacher (born Botz)

Moritz Ernst

Erzsébet Horváth-Puhó

Ulrich Jaehde

Tamara Raschka

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