Research

Reducing patient burden and research cost: the AI4Nof1 project leverages cutting edge developments in (causal, neurosymbolic) reinforcement learning, psychometrics and digital epidemiology to build adaptive personalised treatment regimes for chronic conditions, simultaneously identifying phenotypes and causal pathways while minimising the time and number of measurements needed for patients to find a treatment that is right for them.

curATime is a collaborative, multi-centre effort applying AI methods to high-dimensional biomedical data to promote an understanding of biological processes associated with cardiovascular illness. This exciting project involves highly granular data collected as part of the Gutenberg Health Study — a prospective cohort study of a representative sample ($N=15,000$) of the population of Mainz — including genotyping, DNA methylation, transcriptomics, proteomics and extensive time-varying clinical information.

An ‘event’ can describe any state with a timestamp. Some events are directly observable (death, crime reports, flooding) while others represent latent changes in an underlying process (flares in disease, social unrest, a cyber-attack).

Large language models (LLMs), such as GPT-4, can communicate textually on a variety of topics. They are increasingly ubiquitous in private and public spheres, transforming digital workflows. LLMs are used in many domains, applications, and professional fields, in order to simplify the retrieval of facts and access to information.

The TrustifAI project aims to contribute a set of concrete solutions to improve trustworthiness of AI applications in health and wellbeing at various stages of development lifecycle. A quality platform for development of trustworthy AI applications will enable users to create efficient and effective data science analytics pipelines through a human-in-the-loop approach with the goal of increasing trustworthiness.

The MIRACLE project is developing an innovative multimodal machine learning approach combining clinical, biological and radiological data to identify patients with early-stage resected non-small-cell lung cancer who have a higher risk of relapse.