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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.

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). Predicting events—when, whether and where they occur—is an increasingly important task in science, industry and public policy.

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.