Integration of longitudinal multimodal data in clinical risk prediction using deep learning
Artificial intelligence enables computer programs to execute human-like tasks like image and speech recognition, text translation, and more. These applications are based on deep learning, a method that can recognize patterns in large datasets (e.g. millions of images from the internet) and then make predictions for new cases. In this project, deep learning methods will be developed and applied in a clinical setting. Persons with type 1 diabetes visit their physicians regularly for check-ups and screening for complications. Some of them also monitor their health using wearable devices even between visits. Combining these data creates a unique opportunity for the development of clinical prediction models that can assist clinicians to tailor prevention and treatment. However, complex data of different types (tabular, images, time series) collected repeatedly over time call for the development and application of novel deep learning methods.